Tag Archives: CVPR

Mapping Urban Trees Across North America with the Auto Arborist Dataset

Over four billion people live in cities around the globe, and while most people interact daily with others — at the grocery store, on public transit, at work — they may take for granted their frequent interactions with the diverse plants and animals that comprise fragile urban ecosystems. Trees in cities, called urban forests, provide critical benefits for public health and wellbeing and will prove integral to urban climate adaptation. They filter air and water, capture stormwater runoff, sequester atmospheric carbon dioxide, and limit erosion and drought. Shade from urban trees reduces energy-expensive cooling costs and mitigates urban heat islands. In the US alone, urban forests cover 127M acres and produce ecosystem services valued at $18 billion. But as the climate changes these ecosystems are increasingly under threat.

Census data is typically not comprehensive, covering a subset of public trees and not including those in parks.

Urban forest monitoring — measuring the size, health, and species distribution of trees in cities over time — allows researchers and policymakers to (1) quantify ecosystem services, including air quality improvement, carbon sequestration, and benefits to public health; (2) track damage from extreme weather events; and (3) target planting to improve robustness to climate change, disease and infestation.

However, many cities lack even basic data about the location and species of their trees. Collecting such data via a tree census is costly (a recent Los Angeles census cost $2 million and took 18 months) and thus is typically conducted only by cities with substantial resources. Further, lack of access to urban greenery is a key aspect of urban social inequality, including socioeconomic and racial inequality. Urban forest monitoring enables the quantification of this inequality and the pursuit of its improvement, a key aspect of the environmental justice movement. But machine learning could dramatically lower tree census costs using a combination of street-level and aerial imagery. Such an automated system could democratize access to urban forest monitoring, especially for under-resourced cities that are already disproportionately affected by climate change. While there have been prior efforts to develop automated urban tree species recognition from aerial or street-level imagery, a major limitation has been a lack of large-scale labeled datasets.

Today we introduce the Auto Arborist Dataset, a multiview urban tree classification dataset that, at ~2.6 million trees and >320 genera, is two orders of magnitude larger than those in prior work. To build the dataset, we pulled from public tree censuses from 23 North American cities (shown above) and merged these records with Street View and overhead RGB imagery. As the first urban forest dataset to cover multiple cities, we analyze in detail how forest models can generalize with respect to geographic distribution shifts, crucial to building systems that scale. We are releasing all 2.6M tree records publicly, along with aerial and ground-level imagery for 1M trees.

The 23 cities in the dataset are spread across North America, and are categorized into West, Central, and East regions to enable analysis of spatial and hierarchical generalization.
The number of tree records and genera in the dataset, per city and per region. The holdout city (which is never seen during training in any capacity) for each region is in bold.

The Auto Arborist Dataset
To curate Auto Arborist, we started from existing tree censuses which are provided by many cities online. For each tree census considered, we verified that the data contained GPS locations and genus/species labels, and was available for public use. We then parsed these data into a common format, fixing common data entry errors (such as flipped latitude/longitude) and mapping ground-truth genus names (and their common misspellings or alternate names) to a unified taxonomy. We have chosen to focus on genus prediction (instead of species-level prediction) as our primary task to avoid taxonomic complexity arising from hybrid and subspecies and the fact that there is more universal consensus on genus names than species names.

Next, using the provided geolocation for each tree, we queried an RGB aerial image centered on the tree and all street-level images taken within 2-10 meters around it. Finally, we filtered these images to (1) maximize our chances that the tree of interest is visible from each image and (2) preserve user privacy. This latter concern involved a number of steps including the removal of images that included people as determined by semantic segmentation and manual blurring, among others.

Selected Street View imagery from the Auto Arborist dataset. Green boxes represent tree detections (using a model trained on Open Images) and blue dots represent projected GPS location of the labeled tree.

One of the most important challenges for urban forest monitoring is to do well in cities that were not part of the training set. Vision models must contend with distribution shifts, where the training distribution differs from the test distribution from a new city. Genus distributions vary geographically (e.g., there are more Douglas fir in western Canada than in California) and can also vary based on city size (LA is much larger than Santa Monica and contains many more genera). Another challenge is the long-tailed, fine-grained nature of tree genera, which can be difficult to disambiguate even for human experts, with many genera being quite rare.

The long-tailed distribution across Auto Arborist categories. Most examples come from a few frequent categories, and many categories have far fewer examples. We characterize each genus as frequent, common, or rare based on the number of training examples. Note that the test data is split spatially from the training data within each city, so not all rare genera are seen in the test set.

Finally, there are a number of ways in which tree images can have noise. For one, there is temporal variation in deciduous trees (for example, when aerial imagery includes leaves, but street-level images are bare). Moreover, public arboreal censuses are not always up-to-date. Thus, sometimes trees have died (and are no longer visible) in the time since the tree census was taken. In addition, aerial data quality can be poor (missing or obscured, e.g., by clouds).

Our curation process sought to minimize these issues by (1) only keeping images with sufficient tree pixels, as determined by a semantic segmentation model, (2) only keeping reasonably recent images, and (3) only keeping images where the tree position was sufficiently close to the street level camera. We considered also optimizing for trees seen in spring and summer, but decided seasonal variation could be a useful cue — we thus also released the date of each image to enable the community to explore the effects of seasonal variability.

Benchmark and Evaluation
To evaluate the dataset, we designed a benchmark to measure domain generalization and performance in the long tail of the distribution. We generated training and test splits at three levels. First, we split within each city (based on latitude or longitude) to see how well a city generalizes to itself. Second, we aggregate city-level training sets into three regions, West, Central, and East, holding out one city from each region. Finally, we merge the training sets across the three regions. For each of these splits, we report both accuracy and class-averaged recall for frequent, common and rare species on the corresponding held-out test sets.

Using these metrics, we establish a performance baseline using standard modern convolutional models (ResNet). Our results demonstrate the benefits of a large-scale, geospatially distributed dataset such as Auto Arborist. First, we see that more training data helps — training on the entire dataset is better than training on a region, which is better than training on a single city.

The performance on each city’s test set when training on itself, on the region, and on the full training set.

Second, training on similar cities helps (and thus, having more coverage of cities helps). For example, if focusing on Seattle, then it is better to train on trees in Vancouver than Pittsburgh.

Cross-set performance, looking at the pairwise combination of train and test sets for each city. Note the block-diagonal structure, which highlights regional structure in the dataset.

Third, more data modalities and views help. The best performing models combine inputs from multiple Street View angles and overhead views. There remains much room for improvement, however, and this is where we believe the larger community of researchers can help.

Get Involved
By releasing the Auto Arborist Dataset, we step closer to the goal of affordable urban forest monitoring, enabling the computer vision community to tackle urban forest monitoring at scale for the first time. In the future, we hope to expand coverage to more North American cities (particularly in the South of the US and Mexico) and even worldwide. Further, we are excited to push the dataset to the more fine-grained species level and investigate more nuanced monitoring, including monitoring tree health and growth over time, and studying the effects of environmental factors on urban forests.

For more details, see our CVPR 2022 paper. This dataset is part of Google's broader efforts to empower cities with data about urban forests, through the Environmental Insights Explorer Tree Canopy Lab and is available on our GitHub repo. If you represent a city that is interested in being included in the dataset please email [email protected].

Acknowledgements
We would like to thank our co-authors Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, and Chris Bauer. We also thank Ruth Alcantara, Tanya Birch, and Dan Morris from Google AI for Nature and Society, John Quintero, Stafford Marquardt, Xiaoqi Yin, Puneet Lall, and Matt Manolides from Google Geo, Karan Gill, Tom Duerig, Abhijit Kundu, David Ross, Vighnesh Birodkar from Google Research (Perception team), and Pietro Perona for their support. This work was supported in part by the Resnick Sustainability Institute and was undertaken while Sara Beery was a Student Researcher at Google.

Source: Google AI Blog


Google at CVPR 2022

This week marks the beginning of the premier annual Computer Vision and Pattern Recognition conference (CVPR 2022), held both in-person in New Orleans, LA and virtually. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence across CVPR 2022 with over 80 papers being presented at the main conference and active involvement in a number of conference workshops and tutorials.

If you are attending CVPR this year, please stop by our booth and chat with our researchers who are actively exploring the latest machine learning techniques for application to various areas of machine perception. Our researchers will also be available to talk about and demo several recent efforts, including on-device ML applications with MediaPipe, the Auto Arborist Dataset for urban forest monitoring, and much more.

You can also learn more about our research being presented at CVPR 2022 in the list below (Google affiliations in bold).


Organizing Committee

Tutorials Chairs
Include: Boqing Gong

Website Chairs
Include: AJ Piergiovanni

Area Chairs
Include: Alireza Fathi, Cordelia Schmid, Deqing Sun, Jonathan Barron, Michael Ryoo, Supasorn Suwajanakorn, Susanna Ricco

Diversity, Equity, and Inclusion Chairs
Include: Noah Snavely

Panel Discussion: Embodied Computer Vision
Panelists include: Michael Ryoo


Publications

Learning to Prompt for Continual Learning (see blog post)
Zifeng Wang*, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

GCR: Gradient Coreset Based Replay Buffer Selection for Continual Learning
Rishabh Tiwari, Krishnateja Killamsetty, Rishabh Iyer, Pradeep Shenoy

Zero-Shot Text-Guided Object Generation with Dream Fields
Ajay Jain, Ben Mildenhall, Jonathan T. Barron, Pieter Abbeel, Ben Poole

Towards End-to-End Unified Scene Text Detection and Layout Analysis
Shangbang Long, Siyang Qin, Dmitry Panteleev, Alessandro Bissacco, Yasuhisa Fujii, Michalis Raptis

FLOAT: Factorized Learning of Object Attributes for Improved Multi-object Multi-part Scene Parsing
Rishubh Singh, Pranav Gupta, Pradeep Shenoy, Ravikiran Sarvadevabhatla

LOLNerf: Learn from One Look
Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, Andrea Tagliasacchi

Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
Thiemo Alldieck, Mihai Zanfir, Cristian Sminchisescu

Learning Local Displacements for Point Cloud Completion
Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari

Density-Preserving Deep Point Cloud Compression
Yun He, Xinlin Ren, Danhang Tang, Yinda Zhang, Xiangyang Xue, Yanwei Fu

CMT-DeepLab: Clustering Mask Transformers for Panoptic Segmentation
Qihang Yu*, Huiyu Wang, Dahun Kim, Siyuan Qiao, Maxwell Collins, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

Deformable Sprites for Unsupervised Video Decomposition
Vickie Ye, Zhengqi Li, Richard Tucker, Angjoo Kanazawa, Noah Snavely

Learning with Neighbor Consistency for Noisy Labels
Ahmet Iscen, Jack Valmadre, Anurag Arnab, Cordelia Schmid

Multiview Transformers for Video Recognition
Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, Cordelia Schmid

Kubric: A Scalable Dataset Generator
Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti (Derek) Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan*, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi

3D Moments from Near-Duplicate Photos
Qianqian Wang, Zhengqi Li, David Salesin, Noah Snavely, Brian Curless, Janne Kontkanen

Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Jonathan T. Barron, Ben Mildenhall, Dor Verbin, Pratul P. Srinivasan, Peter Hedman

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Michael Niemeyer*, Jonathan T. Barron, Ben Mildenhall, Mehdi S. M. Sajjadi, Andreas Geiger, Noha Radwan*

Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T. Barron, Pratul P. Srinivasan

IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
Kai Zhang, Fujun Luan, Zhengqi Li, Noah Snavely

MAXIM: Multi-Axis MLP for Image Processing
Zhengzhong Tu*, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, Yinxiao Li

Restormer: Efficient Transformer for High-Resolution Image Restoration
Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang

Burst Image Restoration and Enhancement
Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang

Neural RGB-D Surface Reconstruction
Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, Justus Thies

Scene Representation Transformer: Geometry-Free Novel View Synthesis Through Set-Latent Scene Representations
Mehdi S. M. Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan*, Suhani Vora, Mario Lučić, Daniel Duckworth, Alexey Dosovitskiy*, Jakob Uszkoreit*, Thomas Funkhouser, Andrea Tagliasacchi*

ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
Yongzhi Su, Mahdi Saleh, Torben Fetzer, Jason Rambach, Nassir Navab, Benjamin Busam, Didier Stricker, Federico Tombari

MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision
Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud

GPV-Pose: Category-Level Object Pose Estimation via Geometry-Guided Point-wise Voting
Yan Di, Ruida Zhang, Zhiqiang Lou, Fabian Manhardt, Xiangyang Ji, Nassir Navab, Federico Tombari

Rethinking Deep Face Restoration
Yang Zhao*, Yu-Chuan Su, Chun-Te Chu, Yandong Li, Marius Renn, Yukun Zhu, Changyou Chen, Xuhui Jia

Transferability Metrics for Selecting Source Model Ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink, Vittorio Ferrari

Robust Fine-Tuning of Zero-Shot Models
Mitchell Wortsman, Gabriel Ilharco, Jong Wook Kim, Mike Li, Simon Kornblith, Rebecca Roelofs, Raphael Gontijo Lopes, Hannaneh Hajishirzi, Ali Farhadi, Hongseok Namkoong, Ludwig Schmidt

Block-NeRF: Scalable Large Scene Neural View Synthesis
Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P. Srinivasan, Jonathan T. Barron, Henrik Kretzschmar

Light Field Neural Rendering
Mohammad Suhail*, Carlos Esteves, Leonid Sigal, Ameesh Makadia

Transferability Estimation Using Bhattacharyya Class Separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings, Vittorio Ferrari, Thomas Mensink

Matching Feature Sets for Few-Shot Image Classification
Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné

Which Model to Transfer? Finding the Needle in the Growing Haystack
Cedric Renggli, André Susano Pinto, Luka Rimanic, Joan Puigcerver, Carlos Riquelme, Ce Zhang, Mario Lučić

Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage
Zhuohang Li, Jiaxin Zhang, Luyang Liu, Jian Liu

Estimating Example Difficulty Using Variance of Gradients
Chirag Agarwal, Daniel D'souza, Sara Hooker

More Than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speech (see blog post)
Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, Tal Remez

Robust Outlier Detection by De-Biasing VAE Likelihoods
Kushal Chauhan, Barath Mohan U, Pradeep Shenoy, Manish Gupta, Devarajan Sridharan

Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings
Innfarn Yoo, Huiwen Chang, Xiyang Luo, Ondrej Stava, Ce Liu*, Peyman Milanfar, Feng Yang

Knowledge Distillation: A Good Teacher Is Patient and Consistent
Lucas Beyer, Xiaohua Zhai, Amélie Royer*, Larisa Markeeva*, Rohan Anil, Alexander Kolesnikov

Urban Radiance Fields
Konstantinos Rematas, Andrew Liu, Pratul P. Srinivasan, Jonathan T. Barron, Andrea Tagliasacchi, Thomas Funkhouser, Vittorio Ferrari

Manifold Learning Benefits GANs
Yao Ni, Piotr Koniusz, Richard Hartley, Richard Nock

MaskGIT: Masked Generative Image Transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu*, William T. Freeman

InOut: Diverse Image Outpainting via GAN Inversion
Yen-Chi Cheng, Chieh Hubert Lin, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Ming-Hsuan Yang

Scaling Vision Transformers (see blog post)
Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, Lucas Beyer

Fine-Tuning Image Transformers Using Learnable Memory
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, Andrew Jackson

PokeBNN: A Binary Pursuit of Lightweight Accuracy
Yichi Zhang*, Zhiru Zhang, Lukasz Lew

Bending Graphs: Hierarchical Shape Matching Using Gated Optimal Transport
Mahdi Saleh, Shun-Cheng Wu, Luca Cosmo, Nassir Navab, Benjamin Busam, Federico Tombari

Uncertainty-Aware Deep Multi-View Photometric Stereo
Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

Depth-Supervised NeRF: Fewer Views and Faster Training for Free
Kangle Deng, Andrew Liu, Jun-Yan Zhu, Deva Ramanan

Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
Barbara Roessle, Jonathan T. Barron, Ben Mildenhall, Pratul P. Srinivasan, Matthias Nießner

Trajectory Optimization for Physics-Based Reconstruction of 3D Human Pose from Monocular Video
Erik Gärtner, Mykhaylo Andriluka, Hongyi Xu, Cristian Sminchisescu

Differentiable Dynamics for Articulated 3D Human Motion Reconstruction
Erik Gärtner, Mykhaylo Andriluka, Erwin Coumans, Cristian Sminchisescu

Panoptic Neural Fields: A Semantic Object-Aware Neural Scene Representation
Abhijit Kundu, Kyle Genova, Xiaoqi Yin, Alireza Fathi, Caroline Pantofaru, Leonidas J. Guibas, Andrea Tagliasacchi, Frank Dellaert, Thomas Funkhouser

Pyramid Adversarial Training Improves ViT Performance
Charles Herrmann, Kyle Sargent, Lu Jiang, Ramin Zabih, Huiwen Chang, Ce Liu*, Dilip Krishnan, Deqing Sun

Proper Reuse of Image Classification Features Improves Object Detection
Cristina Vasconcelos, Vighnesh Birodkar, Vincent Dumoulin

SOMSI: Spherical Novel View Synthesis with Soft Occlusion Multi-Sphere Images
Tewodros Habtegebrial, Christiano Gava, Marcel Rogge, Didier Stricker, Varun Jampani

TubeFormer-DeepLab: Video Mask Transformer
Dahun Kim, Jun Xie, Huiyu Wang, Siyuan Qiao, Qihang Yu, Hong-Seok Kim, Hartwig Adam, In So Kweon, Liang-Chieh Chen

Contextualized Spatio-Temporal Contrastive Learning with Self-Supervision
Liangzhe Yuan, Rui Qian*, Yin Cui, Boqing Gong, Florian Schroff, Ming-Hsuan Yang, Hartwig Adam, Ting Liu

When Does Contrastive Visual Representation Learning Work?
Elijah Cole, Xuan Yang, Kimberly Wilber, Oisin Mac Aodha, Serge Belongie

Less Is More: Generating Grounded Navigation Instructions from Landmarks
Su Wang, Ceslee Montgomery, Jordi Orbay, Vighnesh Birodkar, Aleksandra Faust, Izzeddin Gur, Natasha Jaques, Austin Waters, Jason Baldridge, Peter Anderson

Forecasting Characteristic 3D Poses of Human Actions
Christian Diller, Thomas Funkhouser, Angela Dai

BEHAVE: Dataset and Method for Tracking Human Object Interactions
Bharat Lal Bhatnagar, Xianghui Xie, Ilya A. Petrov, Cristian Sminchisescu, Christian Theobalt, Gerard Pons-Moll

Motion-from-Blur: 3D Shape and Motion Estimation of Motion-Blurred Objects in Videos
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Marc Pollefeys

End-to-End Generative Pretraining for Multimodal Video Captioning (see blog post)
Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, Cordelia Schmid

Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation
Jogendra Nath Kundu, Siddharth Seth, Pradyumna YM, Varun Jampani, Anirban Chakraborty, R. Venkatesh Babu

Learning ABCs: Approximate Bijective Correspondence for Isolating Factors of Variation with Weak Supervision
Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia

HumanNeRF: Free-Viewpoint Rendering of Moving People from Monocular Video
Chung-Yi Weng, Brian Curless, Pratul P. Srinivasan, Jonathan T. Barron, Ira Kemelmacher-Shlizerman

Deblurring via Stochastic Refinement
Jay Whang*, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G. Dimakis, Peyman Milanfar

NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
Ben Mildenhall, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron

CoNeRF: Controllable Neural Radiance Fields
Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi

A Conservative Approach for Unbiased Learning on Unknown Biases
Myeongho Jeon, Daekyung Kim, Woochul Lee, Myungjoo Kang, Joonseok Lee

DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection (see blog post)
Yingwei Li*, Adams Wei Yu, Tianjian Meng, Ben Caine, Jiquan Ngiam, Daiyi Peng, Junyang Shen, Yifeng Lu, Denny Zhou, Quoc V. Le, Alan Yuille, Mingxing Tan

Video Frame Interpolation Transformer
Zhihao Shi, Xiangyu Xu, Xiaohong Liu, Jun Chen, Ming-Hsuan Yang

Global Matching with Overlapping Attention for Optical Flow Estimation
Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, Dimitris Metaxas

LiT: Zero-Shot Transfer with Locked-image Text Tuning (see blog post)
Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, Lucas Beyer

Are Multimodal Transformers Robust to Missing Modality?
Mengmeng Ma, Jian Ren, Long Zhao, Davide Testuggine, Xi Peng

3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection
Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Mohammad-Ali Nikouei Mahani, Nassir Navab, Benjamin Busam, Federico Tombari

SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation
Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, Fisher Yu

H4D: Human 4D Modeling by Learning Neural Compositional Representation
Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu

Gravitationally Lensed Black Hole Emission Tomography
Aviad Levis, Pratul P. Srinivasan, Andrew A. Chael, Ren Ng, Katherine L. Bouman

Deep Saliency Prior for Reducing Visual Distraction
Kfir Aberman, Junfeng He, Yossi Gandelsman, Inbar Mosseri, David E. Jacobs, Kai Kohlhoff, Yael Pritch, Michael Rubinstein

The Auto Arborist Dataset: A Large-Scale Benchmark for Multiview Urban Forest Monitoring Under Domain Shift
Sara Beery, Guanhang Wu, Trevor Edwards, Filip Pavetic, Bo Majewski, Shreyasee Mukherjee, Stanley Chan, John Morgan, Vivek Rathod, Jonathan Huang


Workshops

Ethical Considerations in Creative Applications of Computer Vision
Chairs and Advisors: Negar Rostamzadeh, Fernando Diaz, Emily Denton, Mark Diaz, Jason Baldridge

Dynamic Neural Networks Meet Computer Vision Organizers
Invited Speaker: Barret Zoph

Precognition: Seeing Through the Future
Organizer: Utsav Prabhu
Invited Speaker: Sella Nevo

Computer Vision in the Built Environment for the Design, Construction, and Operation of Buildings
Invited Speakers: Thomas Funkhouser, Federico Tombari

Neural Architecture Search: Lightweight NAS Challenge
Invited Speaker: Barret Zoph

Transformers in Vision
Organizer: Lucas Beyer
Invited Speakers and Panelists: Alexander Kolesnikov, Mathilde Caron, Arsha Nagrani, Lucas Beyer

Challenge on Learned Image Compression
Organizers: George Toderici, Johannes Balle, Eirikur Agustsson, Nick Johnston, Fabian Mentzer, Luca Versari
Invited Speaker: Debargha Mukherjee

Embodied AI
Organizers: Anthony Francis, Sören Pirk, Alex Ku, Fei Xia, Peter Anderson
Scientific Advisory Board Members: Alexander Toshev, Jie Tan
Invited Speaker: Carolina Parada

Sight and Sound
Organizers: Arsha Nagrani, William Freeman

New Trends in Image Restoration and Enhancement
Organizers: Ming-Hsuan Yang, Vivek Kwatra, George Toderici

EarthVision: Large Scale Computer Vision for Remote Sensing Imagery
Invited Speaker: John Quinn

LatinX in Computer Vision Research
Organizer: Ruben Villegas

Fine-Grained Visual Categorization
Organizer: Kimberly Wilber

The Art of Robustness: Devil and Angel in Adversarial Machine Learning
Organizer: Florian Tramèr
Invited Speaker: Nicholas Carlini

AI for Content Creation
Organizers: Deqing Sun, Huiwen Chang, Lu Jiang
Invited Speaker: Chitwan Saharia

LOng-form VidEo Understanding
Invited Speaker: Cordelia Schmid

Visual Perception and Learning in an Open World
Invited Speaker: Rahul Sukthankar

Media Forensics
Organizer : Christoph Bregler
Technical Committee Members: Shruti Agarwal, Scott McCloskey, Peng Zhou

Vision Datasets Understanding
Organizer: José Lezama

Embedded Vision
Invited Speaker: Matthias Grundmann

Federated Learning for Computer Vision
Invited Speaker: Zheng Xu

Large Scale Holistic Video Understanding
Organizer: David Ross
Invited Speaker: Anurag Arnab

Learning With Limited Labelled Data for Image and Video Understanding
Invited Speaker: Hugo Larochelle

Bridging the Gap Between Computational Photography and Visual Recognition
Invited Speaker: Xiaohua Zhai

Explainable Artificial Intelligence for Computer Vision
Invited Speaker: Been Kim

Robustness in Sequential Data
Organizers: Sayna Ebrahimi, Kevin Murphy
Invited Speakers: Sayna Ebrahimi, Balaji Lakshminarayanan

Sketch-Oriented Deep Learning
Organizer: David Ha
Invited Speaker: Jonas Jongejan

Multimodal Learning and Applications
Invited Speaker: Cordelia Schmid

Computational Cameras and Displays
Organizer: Tali Dekel
Invited Speaker: Peyman Millanfar

Artificial Social Intelligence
Invited Speaker: Natasha Jaques

VizWiz Grand Challenge: Algorithms to Assist People Who Are Blind
Invited Speaker and Panelist: Andrew Howard

Image Matching: Local Features & Beyond
Organizer: Eduard Trulls

Multi-Agent Behavior: Representation, Modeling, Measurement, and Applications
Organizer: Ting Liu

Efficient Deep Learning for Computer Vision
Organizers: Pete Warden, Andrew Howard, Grace Chu, Jaeyoun Kim

Gaze Estimation and Prediction in the Wild
Organizer: Thabo Beeler


Tutorials

Denoising Diffusion-Based Generative Modeling: Foundations and Applications
Invited Speaker: Ruiqi Gao

Algorithmic Fairness: Why It's Hard and Why It's Interesting
Invited Speaker: Sanmi Koyejo

Beyond Convolutional Neural Networks
Invited Speakers: Neil Houlsby, Alexander Kolesnikov, Xiaohua Zhai

Joint Ego4D and Egocentric Perception, Interaction & Computing
Invited Speaker: Vittorio Ferrari

Deep AUC Maximization
Invited Speakers: Tianbao Yang

Vision-Based Robot Learning
Organizers: Michael S. Ryoo, Andy Zeng, Pete Florence

Graph Machine Learning for Visual Computing
Organizers: Federico Tombari
Invited Speakers: Federico Tombari, Fabian Manhardt



*Work done while at Google.  

Source: Google AI Blog


End-to-end Generative Pre-training for Multimodal Video Captioning

Multimodal video captioning systems utilize both the video frames and speech to generate natural language descriptions (captions) of videos. Such systems are stepping stones towards the longstanding goal of building multimodal conversational systems that effortlessly communicate with users while perceiving environments through multimodal input streams.

Unlike video understanding tasks (e.g., video classification and retrieval) where the key challenge lies in processing and understanding multimodal input videos, the task of multimodal video captioning includes the additional challenge of generating grounded captions. The most widely adopted approach for this task is to train an encoder-decoder network jointly using manually annotated data. However, due to a lack of large-scale, manually annotated data, the task of annotating grounded captions for videos is labor intensive and, in many cases, impractical. Previous research such as VideoBERT and CoMVT pre-train their models on unlabelled videos by leveraging automatic speech recognition (ASR). However, such models often cannot generate natural language sentences because they lack a decoder, and thus only the video encoder is transferred to the downstream tasks.

In “End-to-End Generative Pre-training for Multimodal Video Captioning”, published at CVPR 2022, we introduce a novel pre-training framework for multimodal video captioning. This framework, which we call multimodal video generative pre-training or MV-GPT, jointly trains a multimodal video encoder and a sentence decoder from unlabelled videos by leveraging a future utterance as the target text and formulating a novel bi-directional generation task. We demonstrate that MV-GPT effectively transfers to multimodal video captioning, achieving state-of-the-art results on various benchmarks. Additionally, the multimodal video encoder is competitive for multiple video understanding tasks, such as VideoQA, text-video retrieval, and action recognition.

Future Utterance as an Additional Text Signal
Typically, each training video clip for multimodal video captioning is associated with two different texts: (1) a speech transcript that is aligned with the clip as a part of the multimodal input stream, and (2) a target caption, which is often manually annotated. The encoder learns to fuse information from the transcript with visual contents, and the target caption is used to train the decoder for generation. However, in the case of unlabelled videos, each video clip comes only with a transcript from ASR, without a manually annotated target caption. Moreover, we cannot use the same text (the ASR transcript) for the encoder input and decoder target, since the generation of the target would then be trivial.

MV-GPT circumvents this challenge by leveraging a future utterance as an additional text signal and enabling joint pre-training of the encoder and decoder. However, training a model to generate future utterances that are often not grounded in the input content is not ideal. So we apply a novel bi-directional generation loss to reinforce the connection to the input.

Bi-directional Generation Loss
The issue of non-grounded text generation is mitigated by formulating a bi-directional generation loss that includes forward and backward generation. Forward generation produces future utterances given visual frames and their corresponding transcripts and allows the model to learn to fuse the visual content with its corresponding transcript. Backward generation takes the visual frames and future utterances to train the model to generate a transcript that contains more grounded text of the video clip. Bi-directional generation loss in MV-GPT allows the encoder and the decoder to be trained to handle visually grounded texts.

Bi-directional generation in MV-GPT. A model is trained with two generation losses. In forward generation, the model generates a future utterance (blue boxes) given the frames and the present utterance (red boxes), whereas the present is generated from the future utterance in backward generation. Two special beginning-of-sentence tokens ([BOS-F] and [BOS-B]) initiate forward and backward generation for the decoder.

Results on Multimodal Video Captioning
We compare MV-GPT to existing pre-training losses using the same model architecture, on YouCook2 with standard evaluation metrics (Bleu-4, Cider, Meteor and Rouge-L). While all pre-training techniques improve captioning performances, it is critical to pre-train the decoder jointly to improve model performance. We demonstrate that MV-GPT outperforms the previous state-of-the-art joint pre-training method by over 3.5% with relative gains across all four metrics.

Pre-training Loss Pre-trained Parts Bleu-4 Cider Meteor Rouge-L
No Pre-training N/A 13.25 1.03 17.56 35.48
CoMVT Encoder 14.46 1.24 18.46 37.17
UniVL Encoder + Decoder 19.95 1.98 25.27 46.81
MV-GPT (ours) Encoder + Decoder 21.26 2.14 26.36 48.58
MV-GPT performance across four metrics (Bleu-4, Cider, Meteor and Rouge-L) of different pre-training losses on YouCook2. “Pre-trained parts” indicates which parts of the model are pre-trained — only the encoder or both the encoder and decoder. We reimplement the loss functions of existing methods but use our model and training strategies for a fair comparison.

We transfer a model pre-trained by MV-GPT to four different captioning benchmarks: YouCook2, MSR-VTT, ViTT and ActivityNet-Captions. Our model achieves state-of-the-art performance on all four benchmarks by significant margins. For instance on the Meteor metric, MV-GPT shows over 12% relative improvements in all four benchmarks.

YouCook2 MSR-VTT ViTT ActivityNet-Captions
Best Baseline 22.35 29.90 11.00 10.90
MV-GPT (ours) 27.09 38.66 26.75 12.31
Meteor metric scores of the best baseline methods and MV-GPT on four benchmarks.

Results on Non-generative Video Understanding Tasks
Although MV-GPT is designed to train a generative model for multimodal video captioning, we also find that our pre-training technique learns a powerful multimodal video encoder that can be applied to multiple video understanding tasks, including VideoQA, text-video retrieval and action classification. When compared to the best comparable baseline models, the model transferred from MV-GPT shows superior performance in five video understanding benchmarks on their primary metrics — i.e., top-1 accuracy for VideoQA and action classification benchmarks, and recall at 1 for the retrieval benchmark.

Task Benchmark Best Comparable Baseline MV-GPT
VideoQA MSRVTT-QA 41.5 41.7
ActivityNet-QA 38.9 39.1
Text-Video Retrieval MSR-VTT 33.7 37.3
Action Recognition Kinetics-400 78.9 80.4
Kinetics-600 80.6 82.4
Comparisons of MV-GPT to best comparable baseline models on five video understanding benchmarks. For each dataset we report the widely used primary metric, i.e., MSRVTT-QA and ActivityNet-QA: Top-1 answer accuracy; MSR-VTT: Recall at 1; and Kinetics: Top-1 classification accuracy.

Summary
We introduce MV-GPT, a new generative pre-training framework for multimodal video captioning. Our bi-directional generative objective jointly pre-trains a multimodal encoder and a caption decoder by using utterances sampled at different times in unlabelled videos. Our pre-trained model achieves state-of-the-art results on multiple video captioning benchmarks and other video understanding tasks, namely VideoQA, video retrieval and action classification.

Acknowledgements
This research was conducted by Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab and Cordelia Schmid.

Source: Google AI Blog


Introducing Omnimattes: A New Approach to Matte Generation using Layered Neural Rendering

Image and video editing operations often rely on accurate mattes — images that define a separation between foreground and background. While recent computer vision techniques can produce high-quality mattes for natural images and videos, allowing real-world applications such as generating synthetic depth-of-field, editing and synthesising images, or removing backgrounds from images, one fundamental piece is missing: the various scene effects that the subject may generate, like shadows, reflections, or smoke, are typically overlooked.

In “Omnimatte: Associating Objects and Their Effects in Video”, presented at CVPR 2021, we describe a new approach to matte generation that leverages layered neural rendering to separate a video into layers called omnimattes that include not only the subjects but also all of the effects related to them in the scene. Whereas a typical state-of-the-art segmentation model extracts masks for the subjects in a scene, for example, a person and a dog, the method proposed here can isolate and extract additional details associated with the subjects, such as shadows cast on the ground.

A state-of-the-art segmentation network (e.g., MaskRCNN) takes an input video (left) and produces plausible masks for people and animals (middle), but misses their associated effects. Our method produces mattes that include not only the subjects, but their shadows as well (right; individual channels for person and dog visualized as blue and green).

Also unlike segmentation masks, omnimattes can capture partially-transparent, soft effects such as reflections, splashes, or tire smoke. Like conventional mattes, omnimattes are RGBA images that can be manipulated using widely-available image or video editing tools, and can be used wherever conventional mattes are used, for example, to insert text into a video underneath a smoke trail.

Layered Decomposition of Video
To generate omnimattes, we split the input video into a set of layers: one for each moving subject, and one additional layer for stationary background objects. In the example below, there is one layer for the person, one for the dog, and one for the background. When merged together using conventional alpha blending, these layers reproduce the input video.

Besides reproducing the video, the decomposition must capture the correct effects in each layer. For example, if the person’s shadow appears in the dog’s layer, the merged layers would still reproduce the input video, but inserting an additional element between the person and dog would produce an obvious error. The challenge is to find a decomposition where each subject’s layer captures only that subject’s effects, producing a true omnimatte.

Our solution is to apply our previously developed layered neural rendering approach to train a convolutional neural network (CNN) to map the subject’s segmentation mask and a background noise image into an omnimatte. Due to their structure, CNNs are naturally inclined to learn correlations between image effects, and the stronger the correlation between the effects, the easier for the CNN to learn. In the above video, for example, the spatial relationships between the person and their shadow, and the dog and its shadow, remain similar as they walk from right to left. The relationships change more (hence, the correlations are weaker) between the person and the dog’s shadow, or the dog and the person’s shadow. The CNN learns the stronger correlations first, leading to the correct decomposition.

The omnimatte system is shown in detail below. In a preprocess, the user chooses the subjects and specifies a layer for each. A segmentation mask for each subject is extracted using an off-the-shelf segmentation network, such as MaskRCNN, and camera transformations relative to the background are found using standard camera stabilization tools. A random noise image is defined in the background reference frame and sampled using the camera transformations to produce per-frame noise images. The noise images provide image features that are random but consistently track the background over time, providing a natural input for the CNN to learn to reconstruct the background colors.

The rendering CNN takes as input the segmentation mask and the per-frame noise images and produces the RGB color images and alpha maps, which capture the transparency of each layer. These outputs are merged using conventional alpha-blending to produce the output frame. The CNN is trained from scratch to reconstruct the input frames by finding and associating the effects not captured in a mask (e.g., shadows, reflections or smoke) with the given foreground layer, and to ensure the subject’s alpha roughly includes the segmentation mask. To make sure the foreground layers only capture the foreground elements and none of the stationary background, a sparsity loss is also applied on the foreground alpha.

A new rendering network is trained for each video. Because the network is only required to reconstruct the single input video, it is able to capture fine structures and fast motion in addition to separating the effects of each subject, as seen below. In the walking example, the omnimatte includes the shadow cast on the slats of the park bench. In the tennis example, the thin shadow and even the tennis ball are captured. In the soccer example, the shadow of the player and the ball are decomposed into their proper layers (with a slight error when the player’s foot is occluded by the ball).

This basic model already works well, but one can improve the results by augmenting the input of the CNN with additional buffers such as optical flow or texture coordinates.

Applications
Once the omnimattes are generated, how can they be used? As shown above, we can remove objects, simply by removing their layer from the composition. We can also duplicate objects, by repeating their layer in the composition. In the example below, the video has been “unwrapped” into a panorama, and the horse duplicated several times to produce a stroboscopic photograph effect. Note that the shadow that the horse casts on the ground and onto the obstacle is correctly captured.

A more subtle, but powerful application is to retime the subjects. Manipulation of time is widely used in film, but usually requires separate shots for each subject and a controlled filming environment. A decomposition into omnimattes makes retiming effects possible for everyday videos using only post-processing, simply by independently changing the playback rate of each layer. Since the omnimattes are standard RGBA images, this retiming edit can be done using conventional video editing software.

The video below is decomposed into three layers, one for each child. The children’s initial, unsynchronized jumps are aligned by simply adjusting the playback rate of their layers, producing realistic retiming for the splashes and reflections in the water.

In the original video (left), each child jumps at a different time. After editing (right), everyone jumps together.

It’s important to consider that any novel technique for manipulating images should be developed and applied responsibly, as it could be misused to produce fake or misleading information. Our technique was developed in accordance with our AI Principles and only allows rearrangement of content already present in the video, but even simple rearrangement can significantly alter the effect of a video, as shown in these examples. Researchers should be aware of these risks.

Future Work
There are a number of exciting directions to improve the quality of the omnimattes. On a practical level, this system currently only supports backgrounds that can be modeled as panoramas, where the position of the camera is fixed. When the camera position moves, the panorama model cannot accurately capture the entire background, and some background elements may clutter the foreground layers (sometimes visible in the above figures). Handling fully general camera motion, such as walking through a room or down a street, would require a 3D background model. Reconstruction of 3D scenes in the presence of moving objects and effects is still a difficult research challenge, but one that has seen promising recent progress.

On a theoretical level, the ability of CNNs to learn correlations is powerful, but still somewhat mysterious, and does not always lead to the expected layer decomposition. While our system allows for manual editing when the automatic result is imperfect, a better solution would be to fully understand the capabilities and limitations of CNNs to learn image correlations. Such an understanding could lead to improved denoising, inpainting, and many other video editing applications besides layer decomposition.

Acknowledgements
Erika Lu, from the University of Oxford, developed the omnimatte system during two internships at Google, in collaboration with Google researchers Forrester Cole, Tali Dekel, Michael Rubinstein, William T. Freeman and David Salesin, and University of Oxford researchers Weidi Xie and Andrew Zisserman.

Thank you to the friends and families of the authors who agreed to appear in the example videos. The “horse jump low”, “lucia”, and “tennis” videos are from the DAVIS 2016 dataset. The soccer video is used by permission from Online Soccer Skills. The car drift video was licensed from Shutterstock.

Source: Google AI Blog


Google at CVPR 2021

This week marks the start of the 2021 Conference on Computer Vision and Pattern Recognition (CVPR 2021), the premier annual computer vision event consisting of the main conference, workshops and tutorials. As a leader in computer vision research and a Champion Level Sponsor, Google will have a strong presence at CVPR 2021, with over 70 publications accepted, along with the organization of and participation in multiple workshops and tutorials.

If you are participating in CVPR this year, please visit our virtual booth to learn about Google research into the next generation of intelligent systems that utilize the latest machine learning techniques applied to various areas of machine perception.

You can also learn more about our research being presented at CVPR 2021 in the list below (Google affiliations in bold).

Organizing Committee Members

General Chair: Rahul Sukthankar
Finance Chair: Ramin Zabih
Workshop Chair: Caroline Pantofaru
Area Chairs: Chen Sun, Golnaz Ghiasi, Jonathan Barron, Kostas Rematas, Negar Rostamzadeh, Noah Snavely, Sanmi Koyejo, Tsung-Yi Lin

Publications

Cross-Modal Contrastive Learning for Text-to-Image Generation (see the blog post)
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee*, Yinfei Yang

Learning Graph Embeddings for Compositional Zero-Shot Learning
Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, Zeynep Akata

SPSG: Self-Supervised Photometric Scene Generation From RGB-D Scans
Angela Dai, Yawar Siddiqui, Justus Thies, Julien Valentin, Matthias Nießner

3D-MAN: 3D Multi-Frame Attention Network for Object Detection
Zetong Yang*, Yin Zhou, Zhifeng Chen, Jiquan Ngiam

MIST: Multiple Instance Spatial Transformer
Baptiste Angles, Yuhe Jin, Simon Kornblith, Andrea Tagliasacchi, Kwang Moo Yi

OCONet: Image Extrapolation by Object Completion
Richard Strong Bowen*, Huiwen Chang, Charles Herrmann*, Piotr Teterwak*, Ce Liu, Ramin Zabih

Ranking Neural Checkpoints
Yandong Li, Xuhui Jia, Ruoxin Sang, Yukun Zhu, Bradley Green, Liqiang Wang, Boqing Gong

LipSync3D: Data-Efficient Learning of Personalized 3D Talking Faces From Video Using Pose and Lighting Normalization
Avisek Lahiri, Vivek Kwatra, Christian Frueh, John Lewis, Chris Bregler

Differentiable Patch Selection for Image Recognition
Jean-Baptiste Cordonnier*, Aravindh Mahendran, Alexey Dosovitskiy, Dirk Weissenborn, Jakob Uszkoreit, Thomas Unterthiner

HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences
Feitong Tan, Danhang Tang, Mingsong Dou, Kaiwen Guo, Rohit Pandey, Cem Keskin, Ruofei Du, Deqing Sun, Sofien Bouaziz, Sean Fanello, Ping Tan, Yinda Zhang

VIP-DeepLab: Learning Visual Perception With Depth-Aware Video Panoptic Segmentation (see the blog post)
Siyuan Qiao*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Denys Rozumnyi, Martin R. Oswald, Vittorio Ferrari, Jiri Matas, Marc Pollefeys

HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Lu Mi, Hang Zhao, Charlie Nash, Xiaohan Jin, Jiyang Gao, Chen Sun, Cordelia Schmid, Nir Shavit, Yuning Chai, Dragomir Anguelov

Wide-Baseline Relative Camera Pose Estimation With Directional Learning
Kefan Chen, Noah Snavely, Ameesh Makadia

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Yunyang Xiong, Hanxiao Liu, Suyog Gupta, Berkin Akin, Gabriel Bender, Yongzhe Wang, Pieter-Jan Kindermans, Mingxing Tan, Vikas Singh, Bo Chen

SMURF: Self-Teaching Multi-Frame Unsupervised RAFT With Full-Image Warping
Austin Stone, Daniel Maurer, Alper Ayvaci, Anelia Angelova, Rico Jonschkowski

Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts
Soravit Changpinyo, Piyush Sharma, Nan Ding, Radu Soricut

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
Berk Kaya, Suryansh Kumar, Carlos Oliveira, Vittorio Ferrari, Luc Van Gool

MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
Jennifer Jang, Heinrich Jiang

Repopulating Street Scenes
Yifan Wang*, Andrew Liu, Richard Tucker, Jiajun Wu, Brian L. Curless, Steven M. Seitz, Noah Snavely

MaX-DeepLab: End-to-End Panoptic Segmentation With Mask Transformers (see the blog post)
Huiyu Wang*, Yukun Zhu, Hartwig Adam, Alan Yuille, Liang-Chieh Chen

IBRNet: Learning Multi-View Image-Based Rendering
Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

From Points to Multi-Object 3D Reconstruction
Francis Engelmann*, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari

Learning Compositional Representation for 4D Captures With Neural ODE
Boyan Jiang, Yinda Zhang, Xingkui Wei, Xiangyang Xue, Yanwei Fu

Guided Integrated Gradients: An Adaptive Path Method for Removing Noise
Andrei Kapishnikov, Subhashini Venugopalan, Besim Avci, Ben Wedin, Michael Terry, Tolga Bolukbasi

De-Rendering the World’s Revolutionary Artefacts
Shangzhe Wu*, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa

Spatiotemporal Contrastive Video Representation Learning
Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, Yin Cui

Decoupled Dynamic Filter Networks
Jingkai Zhou, Varun Jampani, Zhixiong Pi, Qiong Liu, Ming-Hsuan Yang

NeuralHumanFVV: Real-Time Neural Volumetric Human Performance Rendering Using RGB Cameras
Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Kaiwen Guo, Minye Wu, Lan Xu

Regularizing Generative Adversarial Networks Under Limited Data
Hung-Yu Tseng*, Lu Jiang, Ce Liu, Ming-Hsuan Yang, Weilong Yang

SceneGraphFusion: Incremental 3D Scene Graph Prediction From RGB-D Sequences
Shun-Cheng Wu, Johanna Wald, Keisuke Tateno, Nassir Navab, Federico Tombari

NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, Jonathan T. Barron

Adversarially Adaptive Normalization for Single Domain Generalization
Xinjie Fan*, Qifei Wang, Junjie Ke, Feng Yang, Boqing Gong, Mingyuan Zhou

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation
Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, Joongkyu Kim

Adversarial Robustness Across Representation Spaces
Pranjal Awasthi, George Yu, Chun-Sung Ferng, Andrew Tomkins, Da-Cheng Juan

Background Splitting: Finding Rare Classes in a Sea of Background
Ravi Teja Mullapudi, Fait Poms, William R. Mark, Deva Ramanan, Kayvon Fatahalian

Searching for Fast Model Families on Datacenter Accelerators
Sheng Li, Mingxing Tan, Ruoming Pang, Andrew Li, Liqun Cheng, Quoc Le, Norman P. Jouppi

Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild With Pose Annotations (see the blog post)
Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski, Matthias Grundmann

CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister

Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food
Quin Thames, Arjun Karpur, Wade Norris, Fangting Xia, Liviu Panait, Tobias Weyand, Jack Sim

CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning
Chen Wei*, Kihyuk Sohn, Clayton Mellina, Alan Yuille, Fan Yang

DetectoRS: Detecting Objects With Recursive Feature Pyramid and Switchable Atrous Convolution
Siyuan Qiao, Liang-Chieh Chen, Alan Yuille

DeRF: Decomposed Radiance Fields
Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, Andrea Tagliasacchi

Variational Transformer Networks for Layout Generation (see the blog post)
Diego Martin Arroyo, Janis Postels, Federico Tombari

Rich Features for Perceptual Quality Assessment of UGC Videos
Yilin Wang, Junjie Ke, Hossein Talebi, Joong Gon Yim, Neil Birkbeck, Balu Adsumilli, Peyman Milanfar, Feng Yang

Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds
Li Yi, Boqing Gong, Thomas Funkhouser

Neural Descent for Visual 3D Human Pose and Shape
Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu

GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Look Before You Speak: Visually Contextualized Utterances
Paul Hongsuck Seo, Arsha Nagrani, Cordelia Schmid

LASR: Learning Articulated Shape Reconstruction From a Monocular Video
Gengshan Yang*, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu

MoViNets: Mobile Video Networks for Efficient Video Recognition
Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, Boqing Gong

No Shadow Left Behind: Removing Objects and Their Shadows Using Approximate Lighting and Geometry
Edward Zhang, Ricardo Martin-Brualla, Janne Kontkanen, Brian Curless

On Robustness and Transferability of Convolutional Neural Networks
Josip Djolonga, Jessica Yung, Michael Tschannen, Rob Romijnders, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Matthias Minderer, Alexander D'Amour, Dan Moldovan, Sylvain Gelly, Neil Houlsby, Xiaohua Zhai, Mario Lucic

Robust and Accurate Object Detection via Adversarial Learning
Xiangning Chen, Cihang Xie, Mingxing Tan, Li Zhang, Cho-Jui Hsieh, Boqing Gong

To the Point: Efficient 3D Object Detection in the Range Image With Graph Convolution Kernels
Yuning Chai, Pei Sun, Jiquan Ngiam, Weiyue Wang, Benjamin Caine, Vijay Vasudevan, Xiao Zhang, Dragomir Anguelov

Bottleneck Transformers for Visual Recognition
Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani

Faster Meta Update Strategy for Noise-Robust Deep Learning
Youjiang Xu, Linchao Zhu, Lu Jiang, Yi Yang

Correlated Input-Dependent Label Noise in Large-Scale Image Classification
Mark Collier, Basil Mustafa, Efi Kokiopoulou, Rodolphe Jenatton, Jesse Berent

Learned Initializations for Optimizing Coordinate-Based Neural Representations
Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P. Srinivasan, Jonathan T. Barron, Ren Ng

Simple Copy-Paste Is a Strong Data Augmentation Method for Instance Segmentation
Golnaz Ghiasi, Yin Cui, Aravind Srinivas*, Rui Qian, Tsung-Yi Lin, Ekin D. Cubuk, Quoc V. Le, Barret Zoph

Function4D: Real-Time Human Volumetric Capture From Very Sparse Consumer RGBD Sensors
Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, Yebin Liu

RSN: Range Sparse Net for Efficient, Accurate LiDAR 3D Object Detection
Pei Sun, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, Dragomir Anguelov

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth

Robust Neural Routing Through Space Partitions for Camera Relocalization in Dynamic Indoor Environments
Siyan Dong, Qingnan Fan, He Wang, Ji Shi, Li Yi, Thomas Funkhouser, Baoquan Chen, Leonidas Guibas

Taskology: Utilizing Task Relations at Scale
Yao Lu, Sören Pirk, Jan Dlabal, Anthony Brohan, Ankita Pasad*, Zhao Chen, Vincent Casser, Anelia Angelova, Ariel Gordon

Omnimatte: Associating Objects and Their Effects in Video
Erika Lu, Forrester Cole, Tali Dekel, Andrew Zisserman, William T. Freeman, Michael Rubinstein

AutoFlow: Learning a Better Training Set for Optical Flow
Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T. Freeman, and Ce Liu

Unsupervised Multi-Source Domain Adaptation Without Access to Source Data
Sk Miraj Ahmed, Dripta S. Raychaudhuri, Sujoy Paul, Samet Oymak, Amit K. Roy-Chowdhury

Meta Pseudo Labels
Hieu Pham, Zihang Dai, Qizhe Xie, Minh-Thang Luong, Quoc V. Le

Spatially-Varying Outdoor Lighting Estimation From Intrinsics
Yongjie Zhu, Yinda Zhang, Si Li, Boxin Shi

Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization
Long Zhao*, Yuxiao Wang, Jiaping Zhao, Liangzhe Yuan, Jennifer J. Sun, Florian Schroff, Hartwig Adam, Xi Peng, Dimitris Metaxas, Ting Liu

Benchmarking Representation Learning for Natural World Image Collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, Oisin Mac Aodha

Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
Tomas Jakab*, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
Vladimir Tankovich, Christian Häne, Yinda Zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz

POSEFusion: Pose-Guided Selective Fusion for Single-View Human Volumetric Capture
Zhe Li, Tao Yu, Zerong Zheng, Kaiwen Guo, Yebin Liu

Workshops (only Google affiliations are noted)

Media Forensics
Organizers: Christoph Bregler

Safe Artificial Intelligence for Automated Driving
Invited Speakers: Been Kim

VizWiz Grand Challenge
Organizers: Meredith Morris

3D Vision and Robotics
Invited Speaker: Andy Zeng

New Trends in Image Restoration and Enhancement Workshop and Challenges on Image and Video Processing
Organizers: Ming-Hsuan Yang Program Committee: George Toderici, Ming-Hsuan Yang

2nd Workshop on Extreme Vision Modeling
Invited Speakers: Quoc Le, Chen Sun

First International Workshop on Affective Understanding in Video
Organizers: Gautam Prasad, Ting Liu

Adversarial Machine Learning in Real-World Computer Vision Systems and Online Challenges
Program Committee: Nicholas Carlini, Nicolas Papernot

Ethical Considerations in Creative Applications of Computer Vision
Invited Speaker: Alex Hanna Organizers: Negar Rostamzadeh, Emily Denton, Linda Petrini

Visual Question Answering Workshop
Invited Speaker: Vittorio Ferrari

Sixth International Skin Imaging Collaboration (ISIC) Workshop on Skin Image Analysis
Invited Speakers: Sandra Avila Organizers: Yuan Liu Steering Committee: Yuan Liu, Dale Webster

The 4th Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition (UG2+) in Conjunction with IEEE CVPR 2021
Invited Speakers: Peyman Milanfar, Chelsea Finn

The 3rd CVPR Workshop on 3D Scene Understanding for Vision, Graphics, and Robotics
Invited Speaker: Andrea Tagliasacchi

Robust Video Scene Understanding: Tracking and Video Segmentation
Organizers: Jordi Pont-Tuset, Sergi Caelles, Jack Valmadre, Alex Bewley

4th Workshop and Challenge on Learned Image Compression
Invited Speaker: Rianne van den Berg Organizers: George Toderici, Lucas Theis, Johannes Ballé, Eirikur Agustsson, Nick Johnston, Fabian Mentzer

The Third Workshop on Precognition: Seeing Through the Future
Invited Speaker: Anelia Angelova
Organizers: Utsav Prabhu Program Committee: Chen Sun, David Ross

Computational Cameras and Displays
Organizers: Tali Dekel Keynote Talks: Paul Debevec Program Committee: Ayan Chakrabarti, Tali Dekel

2nd Embodied AI Workshop
Organizing Committee: Anthony Francis Challenge Organizers: Peter Anderson, Anthony Francis, Alex Ku, Alexander Toshev Scientific Advisory Board: Alexander Toshev

Responsible Computer Vision
Program Committee: Caroline Pantofaru, Utsav Prabhu, Susanna Ricco, Negar Rostamzadeh, Candice Schumann

Dynamic Neural Networks Meets Computer Vision
Invited Speaker: Azalia Mirhoseini

Interactive Workshop on Bridging the Gap between Subjective and Computational Measurements of Machine Creativity
Invited Speaker: David Bau

GAZE 2021: The 3rd International Workshop on Gaze Estimation and Prediction in the Wild
Organizer: Thabo Beeler Program Committee: Thabo Beeler

Sight and Sound
Organizers: William Freeman

Future of Computer Vision Datasets
Invited Speaker: Emily Denton, Caroline Pantofaru

Open World Vision
Invited Speakers: Rahul Sukthankar

The 3rd Workshop on Learning from Unlabeled Videos
Organizers: Anelia Angelova, Honglak Lee Program Committee: AJ Piergiovanni

4th International Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues — With a Focus on Mobile Platform Applications
Organizers: Anelia Angelova

4th Workshop on Efficient Deep Learning for Computer Vision
Invited Speaker: Andrew Howard
Organizers: Pete Warden, Andrew Howard

Second International Workshop on Large Scale Holistic Video Understanding
Invited Speaker: Cordelia Schmid Program Committee: AJ Piergiovanni Organizers: David Ross

Neural Architecture Search 1st Lightweight NAS Challenge and Moving Beyond
Invited Speakers: Sara Sabour

The Second Workshop on Fair, Data-Efficient, and Trusted Computer Vision
Invited Speakers: Gaurav Aggarwal

The 17th Embedded Vision Workshop
General Chair: Anelia Angelova

8th Workshop on Fine-Grained Visual Categorization
Organizers: Christine Kaeser-Chen, Kimberly Wilber

AI for Content Creation
Invited Speaker: Tali Dekel, Jon Barron, Emily Denton Organizers: Deqing Sun

Frontiers of Monocular 3D Perception
Invited Speakers: Anelia Angelova, Cordelia Schmid, Noah Snavely

Beyond Fairness: Towards a Just, Equitable, and Accountable Computer Vision
Organizers: Emily Denton

The 1st Workshop on Future Video Conferencing
Invited Speakers: Chuo-Ling Chang, Sergi Caelles

Tutorials (only Google affiliations are noted)

Tutorial on Fairness Accountability Transparency and Ethics in Computer Vision
Organizer: Emily Denton

Data-Efficient Learning in An Imperfect World
Organizers: Boqing Gong, Ting Chen

Semantic Segmentation of Point Clouds: a Deep Learning Framework for Cultural Heritage
Invited Speaker: Manzil Zaheer

From VQA to VLN: Recent Advances in Vision-and-Language Research
Organizer: Peter Anderson * Indicates work done while at Google

Source: Google AI Blog


Using Variational Transformer Networks to Automate Document Layout Design

Information in a written document is not only conveyed by the meaning of the words contained in it, but also by the overall document layout. Layouts are commonly used to direct the order in which the reader parses a document to enable a better understanding (e.g., with columns or paragraphs), to provide helpful summaries (e.g., with titles) or for aesthetic purposes (e.g., when displaying advertisements).

While these design rules are easy to follow, it is difficult to explicitly define them without quickly needing to include exceptions or encountering ambiguous cases. This makes the automation of document design difficult, as any system with a hardcoded set of production rules will either be overly simplistic and thus incapable of producing original layouts (causing a lack of diversity in the layout of synthesized data), or too complex, with a large set of rules and their accompanying exceptions. In an attempt to solve this challenge, some have proposed machine learning (ML) techniques to synthesize document layouts. However, most ML-based solutions for automatic document design do not scale to a large number of layout components, or they rely on additional information for training, such as the relationships between the different components of a document.

In “Variational Transformer Networks for Layout Generation”, to be presented at CVPR 2021, we create a document layout generation system that scales to an arbitrarily large number of elements and does not require any additional information to capture the relationships between design elements. We use self-attention layers as building blocks of a variational autoencoder (VAE), which is able to model document layout design rules as a distribution, rather than using a set of predetermined heuristics, increasing the diversity of the generated layouts. The resulting Variational Transformer Network (VTN) model is able to extract meaningful relationships between the layout elements (paragraphs, tables, images, etc.), resulting in realistic synthetic documents (e.g., better alignment and margins). We show the effectiveness of this combination across different domains, such as scientific papers, UI layouts, and even furniture arrangements.

VAEs for Layout Generation
The ultimate goal of this system is to infer the design rules for a given type of layout from a collection of examples. If one considers these design rules as the distribution underlying the data, it is possible to use probabilistic models to discover it. We propose doing this with a VAE (widely used for tasks like image generation or anomaly detection), an autoencoder architecture that consists of two distinct subparts, the encoder and decoder. The encoder learns to compress the input to fewer dimensions, retaining only the necessary information to reconstruct the input, while the decoder learns to undo this operation. The compressed representation (also called the bottleneck) can be forced to behave like a known distribution (e.g., a uniform Gaussian). Feeding samples from this a priori distribution to the decoder segment of the network results in outputs similar to the training data.

An additional advantage of the VAE formulation is that it is agnostic to the type of operations used to implement the encoder and decoder segments. As such, we use self-attention layers (typically seen in Transformer architectures) to automatically capture the influence that each layout element has over the rest.

Transformers use self-attention layers to model long, sequenced relationships, often applied to an array of natural language understanding tasks, such as translation and summarization, as well as beyond the language domain in object detection or document layout understanding tasks. The self-attention operation relates every element in a sequence to every other and determines how they influence each other. This property is ideal to model relationships across different elements in a layout without the need for explicit annotations.

In order to synthesize new samples from these relationships, some approaches for layout generation [e.g., 1] and even for other domains [e.g., 2, 3] rely on greedy search algorithms, such as beam search, nucleus sampling or top-k sampling. Since these strategies are often based on exploration rules that tend to favor the most likely outcome at every step, the diversity of the generated samples is not guaranteed. However, by combining self-attention with the VAE’s probabilistic techniques, the model is able to directly learn a distribution from which it can extract new elements.

Modeling the Variational Bottleneck
The bottleneck of a VAE is commonly modeled as a vector representing the input. Since self-attention layers are a sequence-to-sequence architecture, i.e., a sequence of n input elements is mapped onto n output elements, the standard VAE formulation is difficult to apply. Inspired by BERT, we append an auxiliary token to the beginning of the sequence and treat it as the autoencoder bottleneck vector z. During training, the vector associated with this token is the only piece of information passed to the decoder, so the encoder needs to learn how to compress the entire document information in this vector. The decoder then learns to infer the number of elements in the document as well as the locations of each element in the input sequence from this vector alone. This strategy allows us to use standard techniques to regularize the bottleneck, such as the KL divergence.

Decoding
In order to synthesize documents with varying numbers of elements, the network needs to model sequences of arbitrary length, which is not trivial. While self-attention enables the encoder to adapt automatically to any number of elements, the decoder segment does not know the number of elements in advance. We overcome this issue by decoding sequences in an autoregressive way — at every step, the decoder produces an element, which is concatenated to the previously decoded elements (starting with the bottleneck vector z as input), until a special stop element is produced.

A visualization of our proposed architecture

Turning Layouts into Input Data
A document is often composed of several design elements, such as paragraphs, tables, images, titles, footnotes, etc. In terms of design, layout elements are often represented by the coordinates of their enclosing bounding boxes. To make this information easily digestible for a neural network, we define each element with four variables (x, y, width, height), representing the element’s location on the page (x, y) and size (width, height).

Results
We evaluate the performance of the VTN following two criteria: layout quality and layout diversity. We train the model on publicly available document datasets, such as PubLayNet, a collection of scientific papers with layout annotations, and evaluate the quality of generated layouts by quantifying the amount of overlap and alignment between elements. We measure how well the synthetic layouts resemble the training distribution using the Wasserstein distance over the distributions of element classes (e.g., paragraphs, images, etc.) and bounding boxes. In order to capture the layout diversity, we find the most similar real sample for each generated document using the DocSim metric, where a higher number of unique matches to the real data indicates a more diverse outcome.

We compare the VTN approach to previous works like LayoutVAE and Gupta et al. The former is a VAE-based formulation with an LSTM backbone, whereas Gupta et al. use a self-attention mechanism similar to ours, combined with standard search strategies (beam search). The results below show that LayoutVAE struggles to comply with design rules, like strict alignments, as in the case of PubLayNet. Thanks to the self-attention operation, Gupta et al. can model these constraints much more effectively, but the usage of beam search affects the diversity of the results.

IoU Overlap Alignment Wasserstein Class ↓ Wasserstein Box ↓ # Unique Matches ↑
LayoutVAE   0.171 0.321 0.472 - 0.045 241
Gupta et al.   0.039 0.006 0.361 0.018 0.012 546
VTN 0.031 0.017 0.347 0.022 0.012 697
Real Data   0.048 0.007 0.353 - - -
Results on PubLayNet. Down arrows (↓) indicate that a lower score is better, whereas up arrows (↑) indicate higher is better.

We also explore the ability of our approach to learn design rules in other domains, such as Android UIs (RICO), natural scenes (COCO) and indoor scenes (SUN RGB-D). Our method effectively learns the design rules of these datasets and produces synthetic layouts of similar quality as the current state of the art and a higher degree of diversity.

IoU Overlap Alignment Wasserstein Class ↓ Wasserstein Box ↓ # Unique Matches ↑
LayoutVAE   0.193 0.400 0.416 - 0.045 496
Gupta et al.   0.086 0.145 0.366 0.004 0.023 604
VTN 0.115 0.165 0.373 0.007 0.018 680
Real Data   0.084 0.175 0.410 - - -
Results on RICO. Down arrows (↓) indicate that a lower score is better, whereas up arrows (↑) indicate higher is better.
IoU Overlap Alignment Wasserstein Class ↓ Wasserstein Box ↓ # Unique Matches ↑
LayoutVAE   0.325 2.819 0.246 - 0.062 700
Gupta et al.   0.194 1.709 0.334 0.001 0.016 601
VTN 0.197 2.384 0.330 0.0005 0.013 776
Real Data   0.192 1.724 0.347 - - -
Results for COCO. Down arrows (↓) indicate that a lower score is better, whereas up arrows (↑) indicate higher is better.

Below are some examples of layouts produced by our method compared to existing methods. The design rules learned by the network (location, margins, alignment) resemble those of the original data and show a high degree of variability.

LayoutVAE  
Gupta et al.  
VTN
Qualitative results of our method on PubLayNet compared to existing state-of-the-art methods.

Conclusion
In this work we show the feasibility of using self-attention as part of the VAE formulation. We validate the effectiveness of this approach for layout generation, achieving state-of-the-art performance on various datasets and across different tasks. Our research paper also explores alternative architectures for the integration of self-attention and VAEs, exploring non-autoregressive decoding strategies and different types of priors, and analyzes advantages and disadvantages. The layouts produced by our method can help to create synthetic training data for downstream tasks, such as document parsing or automating graphic design tasks. We hope that this work provides a foundation for continued research in this area, as many subproblems are still not completely solved, such as how to suggest styles for the elements in the layout (text font, which image to choose, etc.) or how to reduce the amount of training data necessary for the model to generalize.

AcknowledgementsWe thank our co-author Janis Postels, as well as Alessio Tonioni and Luca Prasso for helping with the design of several of our experiments. We also thank Tom Small for his help creating the animations for this post.

Source: Google AI Blog


Cross-Modal Contrastive Learning for Text-to-Image Generation

Automatic text-to-image synthesis, in which a model is trained to generate images from text descriptions alone, is a challenging task that has recently received significant attention. Its study provides rich insights into how machine learning (ML) models capture visual attributes and relate them to text. Compared to other kinds of inputs to guide image creation, such as sketches, object masks or mouse traces (which we have highlighted in prior work), descriptive sentences are a more intuitive and flexible way to express visual concepts. Hence, a strong automatic text-to-image generation system can also be a useful tool for rapid content creation and could be applied to many other creative applications, similar to other efforts to integrate machine learning into the creation of art (e.g., Magenta).

State-of-the-art image synthesis results are typically achieved using generative adversarial networks (GANs), which train two models — a generator, which tries to create realistic images, and a discriminator, which tries to determine if an image is real or fabricated. Many text-to-image generation models are GANs that are conditioned using text inputs in order to generate semantically relevant images. This is significantly challenging, especially when long, ambiguous descriptions are provided. Moreover, GAN training can be prone to mode collapse, a common failure case for the training process in which the generator learns to produce only a limited set of outputs, so that the discriminator fails to learn robust strategies to recognize fabricated images. To mitigate mode collapse, some approaches use multi-stage refinement networks that iteratively refine an image. However, such systems require multi-stage training, which is less efficient than simpler single-stage end-to-end models. Other efforts rely on hierarchical approaches that first model object layouts before finally synthesizing a realistic image. This requires the use of labeled segmentation data, which can be difficult to obtain.

In “Cross-Modal Contrastive Learning for Text-to-Image Generation,” to appear at CVPR 2021, we present the Cross-Modal Contrastive Generative Adversarial Network (XMC-GAN), which addresses text-to-image generation by learning to maximize the mutual information between image and text using inter-modal (image-text) and intra-modal (image-image) contrastive losses. This approach helps the discriminator to learn more robust and discriminative features, so XMC-GAN is less prone to mode collapse even with one-stage training. Importantly, XMC-GAN achieves state-of-the-art performance with a simple one-stage generation, as compared to previous multi-stage or hierarchical approaches. It is end-to-end trainable, and only requires image-text pairs (as opposed to labeled segmentation or bounding box data).

Contrastive Losses for Text-to-Image Synthesis
The goal of text-to-image synthesis systems is to produce clear, photo-realistic scenes with high semantic fidelity to their conditioned text descriptions. To achieve this, we propose to maximize the mutual information between the corresponding pairs: (1) images (real or generated) with a sentence describing the scene; (2) a generated image and a real image with the same description; and (3) regions of an image (real or generated) and words or phrases associated with them.

In XMC-GAN, this is enforced using contrastive losses. Similar to other GANs, XMC-GAN contains a generator for synthesizing images, and a discriminator that is trained to act as a critic between real and generated images. Three sets of data contribute to the contrastive loss in this system — the real images, the text that describes those images, and the images generated from the text descriptions. The individual loss functions for both the generator and the discriminator are combinations of the loss calculated from whole images with the full text description, combined with the loss calculated from sub-divided images with associated words or phrases. Then, for each batch of training data, we calculate the cosine similarity score between each text description and the real images, and likewise, between each text description and the batch of generated images. The goal is for the matching pairs (both text-to-image and real image-to-generated image) to have high similarity scores and for non-matching pairs to have low scores. Enforcing such a contrastive loss allows the discriminator to learn more robust and discriminative features.

Inter-modal and intra-modal contrastive learning in our proposed XMC-GAN text-to-image synthesis model.

Results
We apply XMC-GAN to three challenging datasets — the first was a collection of MS-COCO descriptions of MS-COCO images, and the other two were datasets annotated with Localized Narratives, one of which covers MS-COCO images (which we call LN-COCO) and the other of which describes Open Images data (LN-OpenImages). We find that XMC-GAN achieves a new state of the art on each. The images generated by XMC-GAN depict scenes that are of higher quality than those generated using other techniques. On MS-COCO, XMC-GAN improves the state-of-the-art Fréchet inception distance (FID) score from 24.7 to 9.3, and is significantly preferred by human evaluators.

Selected qualitative results for generated images on MS-COCO.

Similarly, human raters prefer the image quality in XMC-GAN generated images 77.3% of the time, and 74.1% prefer its image-text alignment compared to three other state-of-the-art approaches (CP-GAN, SD-GAN, and OP-GAN) .

Human evaluation on MS-COCO for image quality and text alignment. Annotators rank (anonymized and order-randomized) generated images from best to worst.

XMC-GAN also generalizes well to the challenging Localized Narratives dataset, which contains longer and more detailed descriptions. Our prior work TReCS tackles text-to-image generation for Localized Narratives using mouse trace inputs to improve image generation quality. Despite not receiving mouse trace annotations, XMC-GAN is able to significantly outperform TReCS on image generation on LN-COCO, improving state-of-the-art FID from 48.7 to 14.1. Incorporating mouse traces and other additional inputs into an end-to-end model such as XMC-GAN would be interesting to study in future work.

In addition, we also train and evaluate on the LN-OpenImages, which is more challenging than MS-COCO because the dataset is much larger with images that cover a broader range of subject matter and that are more complex (8.4 objects on average). To the best of our knowledge, XMC-GAN is the first text-to-image synthesis model that is trained and evaluated on Open Images. XMC-GAN is able to generate high quality results, and sets a strong benchmark FID score of 26.9 on this very challenging task.

Random samples of real and generated images on Open Images.

Conclusion and Future Work
In this work, we present a cross-modal contrastive learning framework to train GAN models for text-to-image synthesis. We investigate several cross-modal contrastive losses that enforce correspondence between image and text. For both human evaluations and quantitative metrics, XMC-GAN establishes a marked improvement over previous models on multiple datasets. It generates high quality images that match their input descriptions well, including for long, detailed narratives, and does so while being a simpler, end-to-end model. We believe that this represents a significant advance towards creative applications for image generation from natural language descriptions. As we continue this research, we are continually evaluating responsible approaches, potential applications and risk mitigation, in accordance with our AI Principles.

Acknowledgements
This is a joint work with Jason Baldridge, Honglak Lee, and Yinfei Yang. We would like to thank Kevin Murphy, Zizhao Zhang, Dilip Krishnan for their helpful feedback. We also want to thank the Google Data Compute team for their work on conducting human evaluations. We are also grateful for general support from the Google Research team.

Source: Google AI Blog


Holistic Video Scene Understanding with ViP-DeepLab

People are able to retrieve the visual information about 3D environments from a picture quite easily — we can identify objects, determine instance sizes, and reconstruct 3D scene layout, all using the limited signals contained in 2D images. This ability is commonly known as the inverse projection problem, which refers to reconstructing the ambiguous mapping from the retinal images to the sources of retinal stimulation. Real-world computer vision applications, such as autonomous driving, heavily rely on these capabilities to localize and identify 3D objects, which require vision models to infer the spatial location, semantic class, and instance label for each 3D point projected to the 2D images. The ability to reconstruct the 3D world from images can be decomposed into two disjoint computer vision tasks: monocular depth estimation (predicting depth from a single image) and video panoptic segmentation (the unification of instance segmentation and semantic segmentation, in the video domain). However, research has generally considered each task separately. Tackling these tasks jointly with a unified computer vision model could result in easier deployment and greater efficiency by sharing computation among multiple tasks.

Driven by the potential value of a model that predicts depth and video panoptic segmentation at the same time, we present “ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation”, accepted to CVPR 2021. In this work, we propose a new task, depth-aware video panoptic segmentation, that aims to simultaneously tackle monocular depth estimation and video panoptic segmentation. For the new task, we present two derived datasets accompanied by a new evaluation metric called depth-aware video panoptic quality (DVPQ). This new metric includes the metrics for depth estimation and video panoptic segmentation, requiring a vision model to simultaneously tackle the two sub-tasks. To this end, we extend Panoptic-DeepLab by adding network branches for depth and video predictions to create ViP-DeepLab, a unified model that jointly performs video panoptic segmentation and monocular depth estimation for each pixel on the image plane, and achieves state-of-the-art performance on several academic datasets for the sub-tasks. This video demonstrates the new task and shows the results of ViP-DeepLab.

Depth-aware video panoptic segmentation results obtained by ViP-DeepLab. Top-left: Video frames used as input. Top-right: Video panoptic segmentation results. Bottom-left: Estimated depth. Bottom-right: Reconstructed 3D points. Each object instance has a unique and temporally consistent label, e.g., pedestrain_1, pedestrain_2, etc. Input images are from the Cityscapes dataset.

Overview
While Panoptic-DeepLab is able to output semantic segmentation, center prediction, and center regression for a single frame, it lacks the capability of depth estimation and temporally consistent instance ID prediction for multiple frames. However, ViP-DeepLab accomplishes this by performing additional predictions from two consecutive frames as input. The first additional output is depth estimation for the first frame, for which it assigns an estimated depth to each pixel. In addition, ViP-DeepLab also performs center regression for two consecutive frames for only the object centers that appear in the first frame. This process is called center offset prediction, and allows ViP-DeepLab to group all the pixels in the two frames to the same object that appears in the first frame. New instances emerge if they are not grouped to the previously detected instances. This process continues for every two consecutive frames (with one overlapping frame) in a video sequence, stitching panoptic predictions together to form predictions with temporally consistent instance IDs. That is, it stitches together where objects are and how they move in a video scene with time.

Outputs of ViP-DeepLab for video panoptic segmentation. Two consecutive frames are concatenated as input. The semantic segmentation output associates each pixel with its semantic classes, while the instance segmentation outputs identify the pixels from two frames associated with an individual object in the first frame. Input images are from the Cityscapes dataset.
Visualization of stitching video panoptic predictions. ViP-DeepLab propagates IDs based on mask intersection-over-union between region pairs. It is capable of tracking objects with large movements, e.g., the cyclist in the image.

Neural Network Design
Building on top of Panoptic-DeepLab, ViP-DeepLab additionally contains two prediction branches: (1) a depth prediction branch, and (2) a next-frame instance branch. Specifically, the depth prediction head is a simple design that predicts depth regression for every pixel, while the next-frame instance branch predicts the center offsets for the pixels in the second frame with respect to the centers in the first frame.

Results
We have tested ViP-DeepLab on multiple popular benchmarks, including Cityscapes-VPS, KITTI Depth Prediction, and KITTI Multi-Object Tracking and Segmentation (MOTS).

Specifically, ViP-DeepLab achieves state-of-the-art (SOTA) results, significantly outperforming previous methods by 5.1% video panoptic quality (VPQ) on the Cityscapes-VPS test set.

Method VPQAll VPQThings VPQStuff
VPSNet 57.4% 45.8% 64.8%
ViP-DeepLab          62.5% (+5.1%)       50.2% (+4.4%)       70.3% (+5.5%)   
VPQ comparison on Cityscapes-VPS test set.

ViP-DeepLab ranks 1st on the KITTI depth prediction benchmark, improving over previous methods by 0.65 SILog (the smaller the better).

Method    SILog       sqErrorRel       absErrorRel       iRMSE   
PWA 11.45 2.30 9.05 12.32
ViP-DeepLab       10.80 2.19 8.94 11.77
Monocular depth estimation comparison on KITTI Depth Prediction benchmark. Note for the depth estimation metrics, the smaller the values, the better the performance. While differences may appear small, the top-performing method on this benchmark usually has a gap in SILog smaller than 0.1.

Additionally, ViP-DeepLab was also 1st on KITTI MOTS pedestrians and 3rd on KITTI MOTS cars ranked by the metric sMOTSA, and now is 3rd for both pedestrians and cars ranked by a newer metric HOTA.

Class Method HOTA
Car PointTrack 62.0%
ViP-DeepLab 76.4% (+14.4%)
Pedestrian       PointTrack 54.4%
ViP-DeepLab          64.3% (+9.9%)   
Performance comparison on KITTI Multi-Object Tracking and Segmentation.

Finally, we also present two new datasets for the new task, depth-aware video panoptic segmentation, and test ViP-DeepLab on them. We hope our ViP-DeepLab results on these two new datasets will serve as a strong baseline for the community to compare against. The results are shown below.

Dataset    DVPQAll       DVPQThings       DVPQStuff   
Cityscapes-DVPS       55.1% 43.3% 63.6%
SemKITTI-DVPS 45.6% 36.6% 52.2%
ViP-DeepLab performance for the task of depth-aware video panoptic segmentation on two new datasets.

Conclusion
With a simple architecture, ViP-DeepLab achieves state-of-the-art performance on video panoptic segmentation, monocular depth estimation, and multi-object tracking and segmentation. We hope that along with MaX-DeepLab, which proposes an efficient dual-path transformer module that allows for end-to-end image panoptic segmentation, ViP-DeepLab is useful to the community and furthers research into a more holistic understanding of scenes in the real world.

Acknowledgements
We would like to thank the support and valuable discussions with Yukun Zhu, Hartwig Adam, and Alan Yuille (co-authors of ViP-DeepLab), as well as Maxwell Collins, and the Mobile Vision team.

Source: Google AI Blog


MaX-DeepLab: Dual-Path Transformers for End-to-End Panoptic Segmentation

Panoptic segmentation is a computer vision task that unifies semantic segmentation (assigning a class label to each pixel) and instance segmentation (detecting and segmenting each object instance). A core task for real-world applications, panoptic segmentation predicts a set of non-overlapping masks along with their corresponding class labels (i.e., category of object, like "car", "traffic light", "road", etc.) and is generally accomplished using multiple surrogate sub-tasks that approximate (e.g., by using box detection methods) the goals of panoptic segmentation.

An example image and its panoptic segmentation masks from the Cityscapes dataset.
Previous methods approximate panoptic segmentation with a tree of surrogate sub-tasks.

Each surrogate sub-task in this proxy tree introduces extra manually-designed modules, such as anchor design rules, box assignment rules, non-maximum suppression (NMS), thing-stuff merging, etc. Although there are good solutions to individual surrogate sub-tasks and modules, undesired artifacts are introduced when these sub-tasks come together in a pipeline for panoptic segmentation, especially in challenging conditions (e.g., two people with similar bounding boxes will trigger NMS, resulting in a missing mask).

Previous efforts, such as DETR, attempted to solve some of these issues by simplifying the box detection sub-task into an end-to-end operation, which is more computationally efficient and results in fewer undesired artifacts. However, the training process still relies heavily on box detection, which does not align with the mask-based definition of panoptic segmentation. Another line of work completely removes boxes from the pipeline, which has the benefit of removing an entire surrogate sub-task along with its associated modules and artifacts. For example, Axial-DeepLab predicts pixel-wise offsets to predefined instance centers, but the surrogate sub-task it uses encounters challenges with highly deformable objects, which have a large variety of shapes (e.g., a cat), or nearby objects with close centers in the image plane, e.g. the image below of a dog seated in a chair.

When the centers of the dog and the chair are close to each other, Axial-DeepLab merges them into one object.

In “MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers”, to be presented at CVPR 2021, we propose the first fully end-to-end approach for the panoptic segmentation pipeline, directly predicting class-labeled masks by extending the Transformer architecture to this computer vision task. Dubbed MaX-DeepLab for extending Axial-DeepLab with a Mask Xformer, our method employs a dual-path architecture that introduces a global memory path, allowing for direct communication with any convolution layers. As a result, MaX-DeepLab shows a significant 7.1% panoptic quality (PQ) gain in the box-free regime on the challenging COCO dataset, closing the gap between box-based and box-free methods for the first time. MaX-DeepLab achieves the state-of-the-art 51.3% PQ on COCO test-dev set, without test time augmentation.

MaX-DeepLab is fully end-to-end: It predicts panoptic segmentation masks directly from images.

End-to-End Panoptic Segmentation
Inspired by DETR, our model directly predicts a set of non-overlapping masks and their corresponding semantic labels, with output masks and classes that are optimized with a PQ-style objective. Specifically, inspired by the evaluation metric, PQ, which is defined as the recognition quality (whether or not the predicted class is correct) times the segmentation quality (whether the predicted mask is correct), we define a similarity metric between two class-labeled masks in the exact same way. The model is directly trained by maximizing this similarity between ground truth masks and predicted masks via one-to-one matching. This direct modeling of panoptic segmentation enables end-to-end training and inference, removing the hand-coded priors that are necessary in existing box-based and box-free methods.

MaX-DeepLab directly predicts N masks and N classes with a CNN and a mask transformer.

Dual-Path Transformer
Instead of stacking a traditional transformer on top of a convolutional neural network (CNN), we propose a dual-path framework for combining CNNs with transformers. Specifically, we enable any CNN layer to read and write to global memory by using a dual-path transformer block. This proposed block adopts all four types of attention between the CNN-path and the memory-path, and can be inserted anywhere in a CNN, enabling communication with the global memory at any layer. MaX-DeepLab also employs a stacked-hourglass-style decoder that aggregates multi-scale features into a high resolution output. The output is then multiplied with the global memory feature, to form the mask set prediction. The classes for the masks are predicted with another branch of the mask transformer.

An overview of the dual-path transformer architecture.

Results
We evaluate MaX-DeepLab on one of the most challenging panoptic segmentation datasets, COCO, against both of the state-of-the-art box-free (Axial-DeepLab) and box-based (DetectoRS) methods. MaX-DeepLab, without test time augmentation, achieves the state-of-the-art result of 51.3% PQ on the test-dev set.

Comparison on COCO test-dev set.

This result surpasses Axial-DeepLab by 7.1% PQ in the box-free regime and DetectoRS by 1.7% PQ, bridging the gap between box-based and box-free methods for the first time. For a consistent comparison with DETR, we also evaluated a lightweight version of MaX-DeepLab that matches the number of parameters and computations of DETR. The lightweight MaX-DeepLab outperforms DETR by 3.3% PQ on the val set and 3.0% PQ on the test-dev set. In addition, we performed extensive ablation studies and analyses on our end-to-end formulation, model scaling, dual-path architectures, and loss functions. Also the extra-long training schedule of DETR is not necessary for MaX-DeepLab.

As an example in the figure below, MaX-DeepLab correctly segments a dog sitting on a chair. Axial-DeepLab relies on a surrogate sub-task of regressing object center offsets. It fails because the centers of the dog and the chair are close to each other. DetectoRS classifies object bounding boxes, instead of masks, as a surrogate sub-task. It filters out the chair mask because the chair bounding box has a low confidence.

A case study for MaX-DeepLab and state-of-the-art box-free and box-based methods.

Another example shows how MaX-DeepLab correctly segments images with challenging conditions.

MaX-DeepLab correctly segments the overlapping zebras. This case is also challenging for other methods since the zebras have similar bounding boxes and nearby object centers. (credit & license)

Conclusion
We have shown for the first time that panoptic segmentation can be trained end-to-end. MaX-DeepLab directly predicts masks and classes with a mask transformer, removing the need for many hand-designed priors such as object bounding boxes, thing-stuff merging, etc. Equipped with a PQ-style loss and a dual-path transformer, MaX-DeepLab achieves the state-of-the-art result on the challenging COCO dataset, closing the gap between box-based and box-free methods.

Acknowledgements
We are thankful to our co-authors, Yukun Zhu, Hartwig Adam, and Alan Yuille. We also thank Maxwell Collins, Sergey Ioffe, Jiquan Ngiam, Siyuan Qiao, Chen Wei, Jieneng Chen, and the Mobile Vision team for the support and valuable discussions.

Source: Google AI Blog


Improving Holistic Scene Understanding with Panoptic-DeepLab



Real-world computer vision applications, such as self-driving cars and robotics, rely on two core tasks — instance segmentation and semantic segmentation. Instance segmentation identifies the class and extent of individual “things” in an image (i.e., countable objects such as people, animals, cars, etc.) and assigns unique identifiers to each (e.g., car_1 and car_2). This is complemented by semantic segmentation, which labels all pixels in an image, including the “things” that are present as well as the surrounding “stuff” (e.g., amorphous regions of similar texture or material, such as grass, sky or road). This latter task, however, does not differentiate between pixels of the same class that belong to different instances of that class.

Panoptic segmentation represents the unification of these two approaches with the goal of assigning a unique value to every pixel in an image that encodes both semantic label and instance ID. Most existing panoptic segmentation algorithms are based on Mask R-CNN, which treats semantic and instance segmentation separately. The instance segmentation step identifies objects in an image, but it often produces object instance masks that overlap one another. To settle the conflict between overlapping instance masks, one commonly employs an heuristic that resolves the discrepancy either based on the mask with a higher confidence score or by use of a pre-defined pairwise relationship between categories (e.g., a tie should always be worn on a person’s front). Additionally, the discrepancies between semantic and instance segmentation results are sorted out by favoring the instance predictions. While these methods generally produce good results, they also introduce heavy latency, which makes it challenging to apply them in real-time applications.

Driven by the need of a real-time panoptic segmentation model, we propose “Panoptic-DeepLab: a simple, fast and strong system for panoptic segmentation”, accepted to CVPR 2020. In this work, we extend the commonly used modern semantic segmentation model, DeepLab, to perform panoptic segmentation using only a small number of additional parameters with the addition of marginal computation overhead. The resulting model, Panoptic-DeepLab, produces semantic and instance segmentation in parallel and without overlap, avoiding the need for the manually designed heuristics adopted by other methods. Additionally, we develop a computationally efficient operation that merges the semantic and instance segmentation results, enabling near real-time end-to-end panoptic segmentation prediction. Unlike methods based on Mask R-CNN, Panoptic-DeepLab does not generate bounding box predictions and requires only three loss functions during training, significantly fewer than current state-of-the-art methods, such as UPSNet, which can have up to eight. Finally, Panoptic-DeepLab has demonstrated state-of-the-art performance on several academic datasets.
Panoptic segmentation results obtained by Panoptic-DeepLab. Left: Video frames used as input to the panoptic segmentation model. Right: Results overlaid on video frames. Each object instance has a unique label, e.g., car_1, car_2, etc.
Overview
Panoptic-DeepLab is simple both conceptually and architecturally. At a high-level, it predicts three outputs. The first is semantic segmentation, in which it assigns a semantic class (e.g., car or grass) to each pixel. However, it does not differentiate between multiple instances of the same class. So, for example, if one car is partly behind another, the pixels associated with both would have the same associated class and would be indistinguishable from one another. This can be addressed by the second two outputs from the model: a center-of-mass prediction for each instance and instance center regression, where the model learns to regress each instance pixel to its center of mass. This latter step ensures that the model associates pixels of a given class to the appropriate instance. The class-agnostic instance segmentation, obtained by grouping predicted foreground pixels to their closest predicted instance centers, is then fused with semantic segmentation by majority-vote rule to generate the final panoptic segmentation.
Overview of Panoptic-DeepLab. Semantic segmentation associates pixels in the image with general classes, while the class-agnostic instance segmentation step identifies the pixels associated with an individual object, regardless of the class. Taken together one gets the final panoptic segmentation image.
Neural Network Design
Panoptic-DeepLab consists of four components: (1) an encoder backbone pre-trained on ImageNet, shared by both the semantic segmentation and instance segmentation branches of the architecture; (2) atrous spatial pyramid pooling (ASPP) modules, similar to that used by DeepLab, which are deployed independently in each branch in order to perform segmentation at a range of spatial scales; (3) similarly decoupled decoder modules specific to each segmentation task; and (4) task-specific prediction heads.

The encoder backbone (1), which has been pre-trained on ImageNet, extracts feature maps that are shared by both the semantic segmentation and instance segmentation branches of the architecture. Typically, the feature map is generated by the backbone model using a standard convolution, which reduces the resolution of the output map to 1/32nd that of the input image and is too coarse for accurate image segmentation. In order to preserve the details of object boundaries, we instead employ atrous convolution, which better retains important features like edges, to generate a feature map with a resolution of 1/16th the original. This is then followed by two ASPP modules (2), one for each branch, which captures multi-scale information for segmentation.

The light-weight decoder modules (3) follow those used in the most recent DeepLab version (DeepLabV3+), but with two modifications. First, we reintroduce an additional low-level feature map (1/8th scale) to the decoder, which helps to preserve spatial information from the original image (e.g., object boundaries) that can be significantly degraded in the final feature map output by the backbone. Second, instead of using the typical 3 × 3 kernel, the decoder employs a 5 × 5 depthwise-separable convolution, which yields somewhat better performance at only a minimal cost in additional overhead.

The two prediction heads (4) are tailored to their task. The semantic segmentation head employs a weighted version of the standard bootstrapped cross entropy loss function, which weights each pixel differently and has proven to be more effective for segmentation of small-scale objects. The instance segmentation head is trained to predict the offsets between the center of mass of an object instance and the surrounding pixels, without knowledge of the object class, forming the class-agnostic instance masks.

Results
To demonstrate the effectiveness of Panoptic-DeepLab, we conduct experiments on three popular academic datasets, Cityscapes, Mapillary Vistas, and COCO datasets. With a simple architecture, Panoptic-DeepLab ranks first in Cityscapes for all three tasks (semantic, instance and panoptic segmentation) without any task-specific fine-tuning. Additionally, Panoptic-DeepLab won the Best Result, Best Paper, and Most Innovative awards on the Mapillary Panoptic Segmentation track at ICCV 2019 Joint COCO and Mapillary Recognition Challenge Workshop. It outperforms the winner of 2018 by a healthy margin of 1.5%. Finally, Panoptic-DeepLab sets new state-of-the-art bottom-up (i.e., box-free) panoptic segmentation results on the COCO dataset, and is also comparable to other methods based on Mask R-CNN.
Accuracy (PQ) vs. Speed (GPU inference time) across three datasets.
Conclusion
With a simple architecture and only three training loss functions, Panoptic-DeepLab achieves state-of-the-art performance while being faster than other methods based on Mask R-CNN. To summarize, we develop the first single-shot panoptic segmentation model that attains state-of-the-art performance on several public benchmarks, and delivers near real time end-to-end inference speed. We hope our simple and effective Panoptic-DeepLab could establish a solid baseline and further benefit the research community.

Acknowledgements
We would like to thank the support and valuable discussions with Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Florian Schroff as well as the Google Mobile Vision team.

Source: Google AI Blog