Tag Archives: Multimodal Learning

MaMMUT: A simple vision-encoder text-decoder architecture for multimodal tasks

Posted by AJ Piergiovanni and Anelia Angelova, Research Scientists, Google Research

Vision-language foundational models are built on the premise of a single pre-training followed by subsequent adaptation to multiple downstream tasks. Two main and disjoint training scenarios are popular: a CLIP-style contrastive learning and next-token prediction. Contrastive learning trains the model to predict if image-text pairs correctly match, effectively building visual and text representations for the corresponding image and text inputs, whereas next-token prediction predicts the most likely next text token in a sequence, thus learning to generate text, according to the required task. Contrastive learning enables image-text and text-image retrieval tasks, such as finding the image that best matches a certain description, and next-token learning enables text-generative tasks, such as Image Captioning and Visual Question Answering (VQA). While both approaches have demonstrated powerful results, when a model is pre-trained contrastively, it typically does not fare well on text-generative tasks and vice-versa. Furthermore, adaptation to other tasks is often done with complex or inefficient methods. For example, in order to extend a vision-language model to videos, some models need to do inference for each video frame separately. This limits the size of the videos that can be processed to only a few frames and does not fully take advantage of motion information available across frames.

Motivated by this, we present “A Simple Architecture for Joint Learning for MultiModal Tasks”, called MaMMUT, which is able to train jointly for these competing objectives and which provides a foundation for many vision-language tasks either directly or via simple adaptation. MaMMUT is a compact, 2B-parameter multimodal model that trains across contrastive, text generative, and localization-aware objectives. It consists of a single image encoder and a text decoder, which allows for a direct reuse of both components. Furthermore, a straightforward adaptation to video-text tasks requires only using the image encoder once and can handle many more frames than prior work. In line with recent language models (e.g., PaLM, GLaM, GPT3), our architecture uses a decoder-only text model and can be thought of as a simple extension of language models. While modest in size, our model outperforms the state of the art or achieves competitive performance on image-text and text-image retrieval, video question answering (VideoQA), video captioning, open-vocabulary detection, and VQA.

The MaMMUT model enables a wide range of tasks such as image-text/text-image retrieval (top left and top right), VQA (middle left), open-vocabulary detection (middle right), and VideoQA (bottom).

Decoder-only model architecture

One surprising finding is that a single language-decoder is sufficient for all these tasks, which obviates the need for both complex constructs and training procedures presented before. For example, our model (presented to the left in the figure below) consists of a single visual encoder and single text-decoder, connected via cross attention, and trains simultaneously on both contrastive and text-generative types of losses. Comparatively, prior work is either not able to handle image-text retrieval tasks, or applies only some losses to only some parts of the model. To enable multimodal tasks and fully take advantage of the decoder-only model, we need to jointly train both contrastive losses and text-generative captioning-like losses.

MaMMUT architecture (left) is a simple construct consisting of a single vision encoder and a single text decoder. Compared to other popular vision-language models — e.g., PaLI (middle) and ALBEF, CoCa (right) — it trains jointly and efficiently for multiple vision-language tasks, with both contrastive and text-generative losses, fully sharing the weights between the tasks.

Decoder two-pass learning

Decoder-only models for language learning show clear advantages in performance with smaller model size (almost half the parameters). The main challenge for applying them to multimodal settings is to unify the contrastive learning (which uses unconditional sequence-level representation) with captioning (which optimizes the likelihood of a token conditioned on the previous tokens). We propose a two-pass approach to jointly learn these two conflicting types of text representations within the decoder. During the first pass, we utilize cross attention and causal masking to learn the caption generation task — the text features can attend to the image features and predict the tokens in sequence. On the second pass, we disable the cross-attention and causal masking to learn the contrastive task. The text features will not see the image features but can attend bidirectionally to all text tokens at once to produce the final text-based representation. Completing this two-pass approach within the same decoder allows for accommodating both types of tasks that were previously hard to reconcile. While simple, we show that this model architecture is able to provide a foundation for multiple multimodal tasks.

MaMMUT decoder-only two-pass learning enables both contrastive and generative learning paths by the same model.

Another advantage of our architecture is that, since it is trained for these disjoint tasks, it can be seamlessly applied to multiple applications such as image-text and text-image retrieval, VQA, and captioning.

Moreover, MaMMUT easily adapts to video-language tasks. Previous approaches used a vision encoder to process each frame individually, which required applying it multiple times. This is slow and restricts the number of frames the model can handle, typically to only 6–8. With MaMMUT, we use sparse video tubes for lightweight adaptation directly via the spatio-temporal information from the video. Furthermore, adapting the model to Open-Vocabulary Detection is done by simply training to detect bounding-boxes via an object-detection head.

Adaptation of the MaMMUT architecture to video tasks (left) is simple and fully reuses the model. This is done by generating a video “tubes” feature representation, similar to image patches, that are projected to lower dimensional tokens and run through the vision encoder. Unlike prior approaches (right) that need to run multiple individual images through the vision encoder, we use it only once.

Results

Our model achieves excellent zero-shot results on image-text and text-image retrieval without any adaptation, outperforming all previous state-of-the-art models. The results on VQA are competitive with state-of-the-art results, which are achieved by much larger models. The PaLI model (17B parameters) and the Flamingo model (80B) have the best performance on the VQA2.0 dataset, but MaMMUT (2B) has the same accuracy as the 15B PaLI.

MaMMUT outperforms the state of the art (SOTA) on Zero-Shot Image-Text (I2T) and Text-Image (T2I) retrieval on both MS-COCO (top) and Flickr (bottom) benchmarks.
Performance on the VQA2.0 dataset is competitive but does not outperform large models such as Flamingo-80B and PalI-17B. Performance is evaluated in the more challenging open-ended text generation setting.

MaMMUT also outperforms the state-of-the-art on VideoQA, as shown below on the MSRVTT-QA and MSVD-QA datasets. Note that we outperform much bigger models such as Flamingo, which is specifically designed for image+video pre-training and is pre-trained with both image-text and video-text data.

MaMMUT outperforms the SOTA models on VideoQA tasks (MSRVTT-QA dataset, top, MSVD-QA dataset, bottom), outperforming much larger models, e.g., the 5B GIT2 or Flamingo, which uses 80B parameters and is pre-trained for both image-language and vision-language tasks.

Our results outperform the state-of-the-art on open-vocabulary detection fine-tuning as is also shown below.

MAMMUT open-vocabulary detection results on the LVIS dataset compared to state-of-the-art methods. We report the average precisions for rare classes (APr) as is previously adopted in the literature.

Key ingredients

We show that joint training of both contrastive and text-generative objectives is not an easy task, and in our ablations we find that these tasks are served better by different design choices. We see that fewer cross-attention connections are better for retrieval tasks, but more are preferred by VQA tasks. Yet, while this shows that our model’s design choices might be suboptimal for individual tasks, our model is more effective than more complex, or larger, models.

Ablation studies showing that fewer cross-attention connections (1-2) are better for retrieval tasks (top), whereas more connections favor text-generative tasks such as VQA (bottom).

Conclusion

We presented MaMMUT, a simple and compact vision-encoder language-decoder model that jointly trains a number of conflicting objectives to reconcile contrastive-like and text-generative tasks. Our model also serves as a foundation for many more vision-language tasks, achieving state-of-the-art or competitive performance on image-text and text-image retrieval, videoQA, video captioning, open-vocabulary detection and VQA. We hope it can be further used for more multimodal applications.


Acknowledgements

The work described is co-authored by: Weicheng Kuo, AJ Piergiovanni, Dahun Kim, Xiyang Luo, Ben Caine, Wei Li, Abhijit Ogale, Luowei Zhou, Andrew Dai, Zhifeng Chen, Claire Cui, and Anelia Angelova. We would like to thank Mojtaba Seyedhosseini, Vijay Vasudevan, Priya Goyal, Jiahui Yu, Zirui Wang, Yonghui Wu, Runze Li, Jie Mei, Radu Soricut, Qingqing Huang, Andy Ly, Nan Du, Yuxin Wu, Tom Duerig, Paul Natsev, Zoubin Ghahramani for their help and support.

Source: Google AI Blog


UniPi: Learning universal policies via text-guided video generation

Posted by Sherry Yang, Research Scientist, and Yilun Du, Student Researcher, Google Research, Brain Team

Building models that solve a diverse set of tasks has become a dominant paradigm in the domains of vision and language. In natural language processing, large pre-trained models, such as PaLM, GPT-3 and Gopher, have demonstrated remarkable zero-shot learning of new language tasks. Similarly, in computer vision, models like CLIP and Flamingo have shown robust performance on zero-shot classification and object recognition. A natural next step is to use such tools to construct agents that can complete different decision-making tasks across many environments.

However, training such agents faces the inherent challenge of environmental diversity, since different environments operate with distinct state action spaces (e.g., the joint space and continuous controls in MuJoCo are fundamentally different from the image space and discrete actions in Atari). This environmental diversity hampers knowledge sharing, learning, and generalization across tasks and environments. Furthermore, it is difficult to construct reward functions across environments, as different tasks generally have different notions of success.

In “Learning Universal Policies via Text-Guided Video Generation”, we propose a Universal Policy (UniPi) that addresses environmental diversity and reward specification challenges. UniPi leverages text for expressing task descriptions and video (i.e., image sequences) as a universal interface for conveying action and observation behavior in different environments. Given an input image frame paired with text describing a current goal (i.e., the next high-level step), UniPi uses a novel video generator (trajectory planner) to generate video with snippets of what an agent’s trajectory should look like to achieve that goal. The generated video is fed into an inverse dynamics model that extracts underlying low-level control actions, which are then executed in simulation or by a real robot agent. We demonstrate that UniPi enables the use of language and video as a universal control interface for generalizing to novel goals and tasks across diverse environments.

Video policies generated by UniPi.
UniPi may be applied to downstream multi-task settings that require combinatorial language generalization, long-horizon planning, or internet-scale knowledge. In the bottom example, UniPi takes the image of the white robot arm from the internet and generates video snippets according to the text description of the goal.


UniPi implementation

To generate a valid and executable plan, a text-to-video model must synthesize a constrained video plan starting at the current observed image. We found it more effective to explicitly constrain a video synthesis model during training (as opposed to only constraining videos at sampling time) by providing the first frame of each video as explicit conditioning context.

At a high level, UniPi has four major components: 1) consistent video generation with first-frame tiling, 2) hierarchical planning through temporal super resolution, 3) flexible behavior synthesis, and 4) task-specific action adaptation. We explain the implementation and benefit of each component in detail below.


Video generation through tiling

Existing text-to-video models like Imagen typically generate videos where the underlying environment state changes significantly throughout the duration. To construct an accurate trajectory planner, it is important that the environment remains consistent across all time points. We enforce environment consistency in conditional video synthesis by providing the observed image as additional context when denoising each frame in the synthesized video. To achieve context conditioning, UniPi directly concatenates each intermediate frame sampled from noise with the conditioned observed image across sampling steps, which serves as a strong signal to maintain the underlying environment state across time.

Text-conditional video generation enables UniPi to train general purpose policies on a wide range of data sources (simulated, real robots and YouTube).


Hierarchical planning

When constructing plans in high-dimensional environments with long time horizons, directly generating a set of actions to reach a goal state quickly becomes intractable due to the exponential growth of the underlying search space as the plan gets longer. Planning methods often circumvent this issue by leveraging a natural hierarchy in planning. Specifically, planning methods first construct coarse plans (the intermediate key frames spread out across time) operating on low-dimensional states and actions, which are then refined into plans in the underlying state and action spaces.

Similar to planning, our conditional video generation procedure exhibits a natural temporal hierarchy. UniPi first generates videos at a coarse level by sparsely sampling videos (“abstractions”) of desired agent behavior along the time axis. UniPi then refines the videos to represent valid behavior in the environment by super-resolving videos across time. Meanwhile, coarse-to-fine super-resolution further improves consistency via interpolation between frames.

Given an input observation and text instruction, we plan a set of images representing agent behavior. Images are converted to actions using an inverse dynamics model.


Flexible behavioral modulation

When planning a sequence of actions for a given sub-goal, one can readily incorporate external constraints to modulate a generated plan. Such test-time adaptability can be implemented by composing a probabilistic prior incorporating properties of the desired plan to specify desired constraints across the synthesized action trajectory, which is also compatible with UniPi. In particular, the prior can be specified using a learned classifier on images to optimize a particular task, or as a Dirac delta distribution on a particular image to guide a plan towards a particular set of states. To train the text-conditioned video generation model, we utilize the video diffusion algorithm, where pre-trained language features from the Text-To-Text Transfer Transformer (T5) are encoded.


Task-specific action adaptation

Given a set of synthesized videos, we train a small task-specific inverse dynamics model to translate frames into a set of low-level control actions. This is independent from the planner and can be done on a separate, smaller and potentially suboptimal dataset generated by a simulator.

Given the input frame and text description of the current goal, the inverse dynamics model synthesizes image frames and generates a control action sequence that predicts the corresponding future actions. An agent then executes inferred low-level control actions via closed-loop control.


Capabilities and evaluation of UniPi

We measure the task success rate on novel language-based goals, and find that UniPi generalizes well to both seen and novel combinations of language prompts, compared to baselines such as Transformer BC, Trajectory Transformer (TT), and Diffuser.

UniPi generalizes well to both seen and novel combinations of language prompts in Place (e.g., “place X in Y”) and Relation (e.g., “place X to the left of Y”) tasks.

Below, we illustrate generated videos on unseen combinations of goals. UniPi is able to synthesize a diverse set of behaviors that satisfy unseen language subgoals:

Generated videos for unseen language goals at test time.


Multi-environment transfer

We measure the task success rate of UniPi and baselines on novel tasks not seen during training. UniPi again outperforms the baselines by a large margin:

UniPi generalizes well to new environments when trained on a set of different multi-task environments.

Below, we illustrate generated videos on unseen tasks. UniPi is further able to synthesize a diverse set of behaviors that satisfy unseen language tasks:

Generated video plans on different new test tasks in the multitask setting.


Real world transfer

Below, we further illustrate generated videos given language instructions on unseen real images. Our approach is able to synthesize a diverse set of different behaviors which satisfy language instructions:

Using internet pre-training enables UniPi to synthesize videos of tasks not seen during training. In contrast, a model trained from scratch incorrectly generates plans of different tasks:

To evaluate the quality of videos generated by UniPi when pre-trained on non-robot data, we use the Fréchet Inception Distance (FID) and Fréchet Video Distance (FVD) metrics. We used Contrastive Language-Image Pre-training scores (CLIPScores) to measure the language-image alignment. We demonstrate that pre-trained UniPi achieves significantly higher FID and FVD scores and a better CLIPScore compared to UniPi without pre-training, suggesting that pre-training on non-robot data helps with generating plans for robots. We report the CLIPScore, FID, and VID scores for UniPi trained on Bridge data, with and without pre-training:


Model (24x40)       CLIPScore ↑       FID ↓       FVD ↓      
No pre-training       24.43 ± 0.04       17.75 ± 0.56       288.02 ± 10.45      
Pre-trained       24.54 ± 0.03       14.54 ± 0.57       264.66 ± 13.64      

Using existing internet data improves video plan predictions under all metrics considered.


The future of large-scale generative models for decision making

The positive results of UniPi point to the broader direction of using generative models and the wealth of data on the internet as powerful tools to learn general-purpose decision making systems. UniPi is only one step towards what generative models can bring to decision making. Other examples include using generative foundation models to provide photorealistic or linguistic simulators of the world in which artificial agents can be trained indefinitely. Generative models as agents can also learn to interact with complex environments such as the internet, so that much broader and more complex tasks can eventually be automated. We look forward to future research in applying internet-scale foundation models to multi-environment and multi-embodiment settings.


Acknowledgements

We’d like to thank all remaining authors of the paper including Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. We would like to thank George Tucker, Douglas Eck, and Vincent Vanhoucke for the feedback on this post and on the original paper.

Source: Google AI Blog


Scaling vision transformers to 22 billion parameters

Posted by Piotr Padlewski and Josip Djolonga, Software Engineers, Google Research

Large Language Models (LLMs) like PaLM or GPT-3 showed that scaling transformers to hundreds of billions of parameters improves performance and unlocks emergent abilities. The biggest dense models for image understanding, however, have reached only 4 billion parameters, despite research indicating that promising multimodal models like PaLI continue to benefit from scaling vision models alongside their language counterparts. Motivated by this, and the results from scaling LLMs, we decided to undertake the next step in the journey of scaling the Vision Transformer.

In “Scaling Vision Transformers to 22 Billion Parameters”, we introduce the biggest dense vision model, ViT-22B. It is 5.5x larger than the previous largest vision backbone, ViT-e, which has 4 billion parameters. To enable this scaling, ViT-22B incorporates ideas from scaling text models like PaLM, with improvements to both training stability (using QK normalization) and training efficiency (with a novel approach called asynchronous parallel linear operations). As a result of its modified architecture, efficient sharding recipe, and bespoke implementation, it was able to be trained on Cloud TPUs with a high hardware utilization1. ViT-22B advances the state of the art on many vision tasks using frozen representations, or with full fine-tuning. Further, the model has also been successfully used in PaLM-e, which showed that a large model combining ViT-22B with a language model can significantly advance the state of the art in robotics tasks.


Architecture

Our work builds on many advances from LLMs, such as PaLM and GPT-3. Compared to the standard Vision Transformer architecture, we use parallel layers, an approach in which attention and MLP blocks are executed in parallel, instead of sequentially as in the standard Transformer. This approach was used in PaLM and reduced training time by 15%.

Secondly, ViT-22B omits biases in the QKV projections, part of the self-attention mechanism, and in the LayerNorms, which increases utilization by 3%. The diagram below shows the modified transformer architecture used in ViT-22B:

ViT-22B transformer encoder architecture uses parallel feed-forward layers, omits biases in QKV and LayerNorm layers and normalizes Query and Key projections.

Models at this scale necessitate “sharding” — distributing the model parameters in different compute devices. Alongside this, we also shard the activations (the intermediate representations of an input). Even something as simple as a matrix multiplication necessitates extra care, as both the input and the matrix itself are distributed across devices. We develop an approach called asynchronous parallel linear operations, whereby communications of activations and weights between devices occur at the same time as computations in the matrix multiply unit (the part of the TPU holding the vast majority of the computational capacity). This asynchronous approach minimizes the time waiting on incoming communication, thus increasing device efficiency. The animation below shows an example computation and communication pattern for a matrix multiplication.

Asynchronized parallel linear operation. The goal is to compute the matrix multiplication y = Ax, but both the matrix A and activation x are distributed across different devices. Here we illustrate how it can be done with overlapping communication and computation across devices. The matrix A is column-sharded across the devices, each holding a contiguous slice, each block represented as Aij. More details are in the paper.

At first, the new model scale resulted in severe training instabilities. The normalization approach of Gilmer et al. (2023, upcoming) resolved these issues, enabling smooth and stable model training; this is illustrated below with example training progressions.

The effect of normalizing the queries and keys (QK normalization) in the self-attention layer on the training dynamics. Without QK normalization (red) gradients become unstable and the training loss diverges.

Results

Here we highlight some results of ViT-22B. Note that in the paper we also explore several other problem domains, like video classification, depth estimation, and semantic segmentation.

To illustrate the richness of the learned representation, we train a text model to produce representations that align text and image representations (using LiT-tuning). Below we show several results for out-of-distribution images generated by Parti and Imagen:

Examples of image+text understanding for ViT-22B paired with a text model. The graph shows normalized probability distribution for each description of an image.

Human object recognition alignment

To find out how aligned ViT-22B classification decisions are with human classification decisions, we evaluated ViT-22B fine-tuned with different resolutions on out-of-distribution (OOD) datasets for which human comparison data is available via the model-vs-human toolbox. This toolbox measures three key metrics: How well do models cope with distortions (accuracy)? How different are human and model accuracies (accuracy difference)? Finally, how similar are human and model error patterns (error consistency)? While not all fine-tuning resolutions perform equally well, ViT-22B variants are state of the art for all three metrics. Furthermore, the ViT-22B models also have the highest ever recorded shape bias in vision models. This means that they mostly use object shape, rather than object texture, to inform classification decisions — a strategy known from human perception (which has a shape bias of 96%). Standard models (e.g., ResNet-50, which has aa ~20–30% shape bias) often classify images like the cat with elephant texture below according to the texture (elephant); models with a high shape bias tend to focus on the shape instead (cat). While there are still many important differences between human and model perception, ViT-22B shows increased similarities to human visual object recognition.

Cat or elephant? Car or clock? Bird or bicycle? Example images with the shape of one object and the texture of a different object, used to measure shape/texture bias.
Shape bias evaluation (higher = more shape-biased). Many vision models have a low shape / high texture bias, whereas ViT-22B fine-tuned on ImageNet (red, green, blue trained on 4B images as indicated by brackets after model names, unless trained on ImageNet only) have the highest shape bias recorded in a ML model to date, bringing them closer to a human-like shape bias.

Out-of-distribution performance

Measuring performance on OOD datasets helps assess generalization. In this experiment we construct label-maps (mappings of labels between datasets) from JFT to ImageNet and also from ImageNet to different out-of-distribution datasets like ObjectNet (results after pre-training on this data shown in the left curve below). Then the models are fully fine-tuned on ImageNet.

We observe that scaling Vision Transformers increases OOD performance: even though ImageNet accuracy saturates, we see a significant increase on ObjectNet from ViT-e to ViT-22B (shown by the three orange dots in the upper right below).

Even though ImageNet accuracy saturates, we see a significant increase in performance on ObjectNet from ViT-e/14 to ViT-22B.

Linear probe

Linear probe is a technique where a single linear layer is trained on top of a frozen model. Compared to full fine-tuning, this is much cheaper to train and easier to set up. We observed that the linear probe of ViT-22B performance approaches that of state-of-the-art full fine-tuning of smaller models using high-resolution images (training with higher resolution is generally much more expensive, but for many tasks it yields better results). Here are results of a linear probe trained on the ImageNet dataset and evaluated on the ImageNet validation dataset and other OOD ImageNet datasets.

Linear probe results trained on ImageNet, evaluated on Imagenet-ReaL, ImageNet-v2, ObjectNet, ImageNet-R and ImageNet-A datasets. High-resolution fine-tuned ViT-e/14 provided as a reference.

Distillation

The knowledge of the bigger model can be transferred to a smaller model using the distillation method. This is helpful as big models are slower and more expensive to use. We found that ViT-22B knowledge can be transferred to smaller models like ViT-B/16 and ViT-L/16, achieving a new state of the art on ImageNet for those model sizes.


Model Approach (dataset) ImageNet1k Accuracy
ViT-B/16       Transformers for Image Recognition at Scale (JFT)       84.2
Scaling Vision Transformers (JFT) 86.6
DeiT III: Revenge of the ViT (INet21k) 86.7
Distilled from ViT-22B (JFT) 88.6
   
ViT-L/16 Transformers for Image Recognition at Scale (JFT) 87.1
Scaling Vision Transformers (JFT) 88.5
DeiT III: Revenge of the ViT (INet21k) 87.7
Distilled from ViT-22B (JFT) 89.6


Fairness and bias

ML models can be susceptible to unintended unfair biases, such as picking up spurious correlations (measured using demographic parity) or having performance gaps across subgroups. We show that scaling up the size helps in mitigating such issues.

First, scale offers a more favorable tradeoff frontier — performance improves with scale even when the model is post-processed after training to control its level of demographic parity below a prescribed, tolerable level. Importantly, this holds not only when performance is measured in terms of accuracy, but also other metrics, such as calibration, which is a statistical measure of the truthfulness of the model's estimated probabilities. Second, classification of all subgroups tends to improve with scale as demonstrated below. Third, ViT-22B reduces the performance gap across subgroups.


Top: Accuracy for each subgroup in CelebA before debiasing. Bottom: The y-axis shows the absolute difference in performance across the two specific subgroups highlighted in this example: females and males. ViT-22B has a small gap in performance compared to smaller ViT architectures.

Conclusions

We have presented ViT-22B, currently the largest vision transformer model at 22 billion parameters. With small but critical changes to the original architecture, we achieved excellent hardware utilization and training stability, yielding a model that advances the state of the art on several benchmarks. Great performance can be achieved using the frozen model to produce embeddings and then training thin layers on top. Our evaluations further show that ViT-22B shows increased similarities to human visual perception when it comes to shape and texture bias, and offers benefits in fairness and robustness, when compared to existing models.


Acknowledgements

This is a joint work of Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin Fathy, Elsayed Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers Jeremiah Harmsen, and Neil Houlsby

We would like to thank Jasper Uijlings, Jeremy Cohen, Arushi Goel, Radu Soricut, Xingyi Zhou, Lluis Castrejon, Adam Paszke, Joelle Barral, Federico Lebron, Blake Hechtman, and Peter Hawkins. Their expertise and unwavering support played a crucial role in the completion of this paper. We also acknowledge the collaboration and dedication of the talented researchers and engineers at Google Research.

1Note: ViT-22B has 54.9% model FLOPs utilization (MFU) while PaLM reported 46.2% MFU and we measured 44.0% MFU for ViT-e on the same hardware. ↩

Source: Google AI Blog


Vid2Seq: a pretrained visual language model for describing multi-event videos

Posted by Antoine Yang, Student Researcher, and Arsha Nagrani, Research Scientist, Google Research, Perception team

Videos have become an increasingly important part of our daily lives, spanning fields such as entertainment, education, and communication. Understanding the content of videos, however, is a challenging task as videos often contain multiple events occurring at different time scales. For example, a video of a musher hitching up dogs to a dog sled before they all race away involves a long event (the dogs pulling the sled) and a short event (the dogs being hitched to the sled). One way to spur research in video understanding is via the task of dense video captioning, which consists of temporally localizing and describing all events in a minutes-long video. This differs from single image captioning and standard video captioning, which consists of describing short videos with a single sentence.

Dense video captioning systems have wide applications, such as making videos accessible to people with visual or auditory impairments, automatically generating chapters for videos, or improving the search of video moments in large databases. Current dense video captioning approaches, however, have several limitations — for example, they often contain highly specialized task-specific components, which make it challenging to integrate them into powerful foundation models. Furthermore, they are often trained exclusively on manually annotated datasets, which are very difficult to obtain and hence are not a scalable solution.

In this post, we introduce “Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning”, to appear at CVPR 2023. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the same output sequence. In order to pre-train this unified model, we leverage unlabeled narrated videos by reformulating sentence boundaries of transcribed speech as pseudo-event boundaries, and using the transcribed speech sentences as pseudo-event captions. The resulting Vid2Seq model pre-trained on millions of narrated videos improves the state of the art on a variety of dense video captioning benchmarks including YouCook2, ViTT and ActivityNet Captions. Vid2Seq also generalizes well to the few-shot dense video captioning setting, the video paragraph captioning task, and the standard video captioning task. Finally, we have also released the code for Vid2Seq here.

Vid2Seq is a visual language model that predicts dense event captions together with their temporal grounding in a video by generating a single sequence of tokens.

A visual language model for dense video captioning

Multimodal transformer architectures have improved the state of the art on a wide range of video tasks, such as action recognition. However it is not straightforward to adapt such an architecture to the complex task of jointly localizing and captioning events in minutes-long videos.

For a general overview of how we achieve this, we augment a visual language model with special time tokens (like text tokens) that represent discretized timestamps in the video, similar to Pix2Seq in the spatial domain. Given visual inputs, the resulting Vid2Seq model can both take as input and generate sequences of text and time tokens. First, this enables the Vid2Seq model to understand the temporal information of the transcribed speech input, which is cast as a single sequence of tokens. Second, this allows Vid2Seq to jointly predict dense event captions and temporally ground them in the video while generating a single sequence of tokens.

The Vid2Seq architecture includes a visual encoder and a text encoder, which encode the video frames and the transcribed speech input, respectively. The resulting encodings are then forwarded to a text decoder, which autoregressively predicts the output sequence of dense event captions together with their temporal localization in the video. The architecture is initialized with a powerful visual backbone and a strong language model.

Vid2Seq model overview: We formulate dense event captioning as a sequence-to-sequence problem, using special time tokens to allow the model to seamlessly understand and generate sequences of tokens containing both textual semantic information and temporal localization information grounding each text sentence in the video.

Large-scale pre-training on untrimmed narrated videos

Due to the dense nature of the task, the manual collection of annotations for dense video captioning is particularly expensive. Hence we pre-train the Vid2Seq model using unlabeled narrated videos, which are easily available at scale. In particular, we use the YT-Temporal-1B dataset, which includes 18 million narrated videos covering a wide range of domains.

We use transcribed speech sentences and their corresponding timestamps as supervision, which are cast as a single sequence of tokens. We pre-train Vid2Seq with a generative objective that teaches the decoder to predict the transcribed speech sequence given visual inputs only, and a denoising objective that encourages multimodal learning by requiring the model to predict masked tokens given a noisy transcribed speech sequence and visual inputs. In particular, noise is added to the speech sequence by randomly masking out spans of tokens.

Vid2Seq is pre-trained on unlabeled narrated videos with a generative objective (top) and a denoising objective (bottom).

Results on downstream dense video captioning benchmarks

The resulting pre-trained Vid2Seq model can be fine-tuned on downstream tasks with a simple maximum likelihood objective using teacher forcing (i.e., predicting the next token given previous ground-truth tokens). After fine-tuning, Vid2Seq notably improves the state of the art on three standard downstream dense video captioning benchmarks (ActivityNet Captions, YouCook2 and ViTT) and two video clip captioning benchmarks (MSR-VTT, MSVD). In our paper we provide additional ablation studies, qualitative results, as well as results in the few-shot settings and in the video paragraph captioning task.

Comparison to state-of-the-art methods for dense video captioning (left) and for video clip captioning (right), on the CIDEr metric (higher is better).

Conclusion

We introduce Vid2Seq, a novel visual language model for dense video captioning that simply predicts all event boundaries and captions as a single sequence of tokens. Vid2Seq can be effectively pretrained on unlabeled narrated videos at scale, and achieves state-of-the-art results on various downstream dense video captioning benchmarks. Learn more from the paper and grab the code here.


Acknowledgements

This research was conducted by Antoine Yang, Arsha Nagrani, Paul Hongsuck Seo, Antoine Miech, Jordi Pont-Tuset, Ivan Laptev, Josef Sivic and Cordelia Schmid.

Source: Google AI Blog


PaLM-E: An embodied multimodal language model

Posted by Danny Driess, Student Researcher, and Pete Florence, Research Scientist, Robotics at Google

Recent years have seen tremendous advances across machine learning domains, from models that can explain jokes or answer visual questions in a variety of languages to those that can produce images based on text descriptions. Such innovations have been possible due to the increase in availability of large scale datasets along with novel advances that enable the training of models on these data. While scaling of robotics models has seen some success, it is outpaced by other domains due to a lack of datasets available on a scale comparable to large text corpora or image datasets.

Today we introduce PaLM-E, a new generalist robotics model that overcomes these issues by transferring knowledge from varied visual and language domains to a robotics system. We began with PaLM, a powerful large language model, and “embodied” it (the “E” in PaLM-E), by complementing it with sensor data from the robotic agent. This is the key difference from prior efforts to bring large language models to robotics — rather than relying on only textual input, with PaLM-E we train the language model to directly ingest raw streams of robot sensor data. The resulting model not only enables highly effective robot learning, but is also a state-of-the-art general-purpose visual-language model, while maintaining excellent language-only task capabilities.




An embodied  language model, and also a visual-language generalist

On the one hand, PaLM-E was primarily developed to be a model for robotics, and it solves a variety of tasks on multiple types of robots and for multiple modalities (images, robot states, and neural scene representations). At the same time, PaLM-E is a generally-capable vision-and-language model. It can perform visual tasks, such as describing images, detecting objects, or classifying scenes, and is also proficient at language tasks, like quoting poetry, solving math equations or generating code.

PaLM-E combines our most recent large language model, PaLM, together with one of our most advanced vision models, ViT-22B. The largest instantiation of this approach, built on PaLM-540B, is called PaLM-E-562B and sets a new state of the art on the visual-language OK-VQA benchmark, without task-specific fine-tuning, and while retaining essentially the same general language performance as PaLM-540B.


How does PaLM-E work?

Technically, PaLM-E works by injecting observations into a pre-trained language model. This is realized by transforming sensor data, e.g., images, into a representation through a procedure that is comparable to how words of natural language are processed by a language model.

Language models rely on a mechanism to represent text mathematically in a way that neural networks can process. This is achieved by first splitting the text into so-called tokens that encode (sub)words, each of which is associated with a high-dimensional vector of numbers, the token embedding. The language model is then able to apply mathematical operations (e.g., matrix multiplication) on the resulting sequence of vectors to predict the next, most likely word token. By feeding the newly predicted word back to the input, the language model can iteratively generate a longer and longer text.

The inputs to PaLM-E are text and other modalities — images, robot states, scene embeddings, etc. — in an arbitrary order, which we call "multimodal sentences". For example, an input might look like, "What happened between <img_1> and <img_2>?", where <img_1> and <img_2> are two images. The output is text generated auto-regressively by PaLM-E, which could be an answer to a question, or a sequence of decisions in text form.

PaLM-E model architecture, showing how PaLM-E ingests different modalities (states and/or images) and addresses tasks through multimodal language modeling.

The idea of PaLM-E is to train encoders that convert a variety of inputs into the same space as the natural word token embeddings. These continuous inputs are mapped into something that resembles "words" (although they do not necessarily form discrete sets). Since both the word and image embeddings now have the same dimensionality, they can be fed into the language model.

We initialize PaLM-E for training with pre-trained models for both the language (PaLM) and vision components (Vision Transformer, a.k.a. ViT). All parameters of the model can be updated during training.


Transferring knowledge from large-scale training to robots

PaLM-E offers a new paradigm for training a generalist model, which is achieved by framing robot tasks and vision-language tasks together through a common representation: taking images and text as input, and outputting text. A key result is that PaLM-E attains significant positive knowledge transfer from both the vision and language domains, improving the effectiveness of robot learning.

Positive transfer of knowledge from general vision-language tasks results in more effective robot learning, shown for three different robot embodiments and domains.

Results show that PaLM-E can address a large set of robotics, vision and language tasks simultaneously without performance degradation compared to training individual models on individual tasks. Further, the visual-language data actually significantly improves the performance of the robot tasks. This transfer enables PaLM-E to learn robotics tasks efficiently in terms of the number of examples it requires to solve a task.


Results

We evaluate PaLM-E on three robotic environments, two of which involve real robots, as well as general vision-language tasks such as visual question answering (VQA), image captioning, and general language tasks. When PaLM-E is tasked with making decisions on a robot, we pair it with a low-level language-to-action policy to translate text into low-level robot actions.

In the first example below, a person asks a mobile robot to bring a bag of chips to them. To successfully complete the task, PaLM-E produces a plan to find the drawer and open it and then responds to changes in the world by updating its plan as it executes the task. In the second example, the robot is asked to grab a green block. Even though the block has not been seen by that robot, PaLM-E still generates a step-by-step plan that generalizes beyond the training data of that robot.

  
PaLM-E controls a mobile robot operating in a kitchen environment. Left: The task is to get a chip bag. PaLM-E shows robustness against adversarial disturbances, such as putting the chip bag back into the drawer. Right: The final steps of executing a plan to retrieve a previously unseen block (green star). This capability is facilitated by transfer learning from the vision and language models.

In the second environment below, the same PaLM-E model solves very long-horizon, precise tasks, such as “sort the blocks by colors into corners,” on a different type of robot. It directly looks at the images and produces a sequence of shorter textually-represented actions — e.g., “Push the blue cube to the bottom right corner,” “Push the blue triangle there too.” — long-horizon tasks that were out of scope for autonomous completion, even in our own most recent models. We also demonstrate the ability to generalize to new tasks not seen during training time (zero-shot generalization), such as pushing red blocks to the coffee cup.

  
PaLM-E controlling a tabletop robot to successfully complete long-horizon tasks.

The third robot environment is inspired by the field of task and motion planning (TAMP), which studies combinatorially challenging planning tasks (rearranging objects) that confront the robot with a very high number of possible action sequences. We show that with a modest amount of training data from an expert TAMP planner, PaLM-E is not only able to also solve these tasks, but it also leverages visual and language knowledge transfer in order to more effectively do so.

  
PaLM-E produces plans for a task and motion planning environment.

As a visual-language generalist, PaLM-E is a competitive model, even compared with the best vision-language-only models, including Flamingo and PaLI. In particular, PaLM-E-562B achieves the highest number ever reported on the challenging OK-VQA dataset, which requires not only visual understanding but also external knowledge of the world. Further, this result is reached with a generalist model, without fine-tuning specifically on only that task.

PaLM-E exhibits capabilities like visual chain-of-thought reasoning in which the model breaks down its answering process in smaller steps, an ability that has so far only been demonstrated in the language-only domain. The model also demonstrates the ability to perform inference on multiple images although being trained on only single-image prompts. The image of the New York Knicks and Boston Celtics is under the terms CC-by-2.0 and was posted to Flickr by kowarski. The image of Kobe Bryant is in the Public Domain. The other images were taken by us.

Conclusion

PaLM-E pushes the boundaries of how generally-capable models can be trained to simultaneously address vision, language and robotics while also being capable of transferring knowledge from vision and language to the robotics domain. There are additional topics investigated in further detail in the paper, such as how to leverage neural scene representations with PaLM-E and also the extent to which PaLM-E, with greater model scale, experiences less catastrophic forgetting of its language capabilities.

PaLM-E not only provides a path towards building more capable robots that benefit from other data sources, but might also be a key enabler to other broader applications using multimodal learning, including the ability to unify tasks that have so far seemed separate.


Acknowledgements

This work was done in collaboration across several teams at Google, including the Robotics at Google team and the Brain team, and with TU Berlin. Co-authors: Igor Mordatch, Andy Zeng, Aakanksha Chowdhery, Klaus Greff, Mehdi S. M. Sajjadi, Daniel Duckworth, Corey Lynch, Ayzaan Wahid, Jonathan Tompson, Fei Xia, Brian Ichter, Karol Hausman, Tianhe Yu, Quan Vuong, Yevgen Chebotar, Wenlong Huang, Pierre Sermanet, Sergey Levine, Vincent Vanhoucke, and Marc Toussiant. Danny is a PhD student advised by Marc Toussaint at TU Berlin. We also would like to thank several other colleagues for their advice and help, including Xi Chen, Etienne Pot, Sebastian Goodman, Maria Attarian, Ted Xiao, Keerthana Gopalakrishnan, Kehang Han, Henryk Michalewski, Neil Houlsby, Basil Mustafa, Justin Gilmer, Yonghui Wu, Erica Moreira, Victor Gomes, Tom Duerig, Mario Lucic, Henning Meyer, and Kendra Byrne.

Source: Google AI Blog


Crossmodal-3600 — Multilingual Reference Captions for Geographically Diverse Images

Posted by Ashish Thapliyal, Software Engineer, and Jordi Pont-Tuset, Research Scientist, Google Research

Image captioning is the machine learning task of automatically generating a fluent natural language description for a given image. This task is important for improving accessibility for visually impaired users and is a core task in multimodal research encompassing both vision and language modeling.

However, datasets for image captioning are primarily available in English. Beyond that, there are only a few datasets covering a limited number of languages that represent just a small fraction of the world’s population. Further, these datasets feature images that severely under-represent the richness and diversity of cultures from across the globe. These aspects have hindered research on image captioning for a wide variety of languages, and directly hamper the deployment of accessibility solutions for a large potential audience around the world.

Today we present and make publicly available the Crossmodal 3600 (XM3600) image captioning evaluation dataset as a robust benchmark for multilingual image captioning that enables researchers to reliably compare research contributions in this emerging field. XM3600 provides 261,375 human-generated reference captions in 36 languages for a geographically diverse set of 3600 images. We show that the captions are of high quality and the style is consistent across languages.

The Crossmodal 3600 dataset includes reference captions in 36 languages for each of a geographically diverse set of 3600 images. All images used with permission under the CC-BY 2.0 license.

Overview of the Crossmodal 3600 Dataset
Creating large training and evaluation datasets in multiple languages is a resource-intensive endeavor. Recent work has shown that it is feasible to build multilingual image captioning models trained on machine-translated data with English captions as the starting point. However, some of the most reliable automatic metrics for image captioning are much less effective when applied to evaluation sets with translated image captions, resulting in poorer agreement with human evaluations compared to the English case. As such, trustworthy model evaluation at present can only be based on extensive human evaluation. Unfortunately, such evaluations usually cannot be replicated across different research efforts, and therefore do not offer a fast and reliable mechanism to automatically evaluate multiple model parameters and configurations (e.g., model hill climbing) or to compare multiple lines of research.

XM3600 provides 261,375 human-generated reference captions in 36 languages for a geographically diverse set of 3600 images from the Open Images dataset. We measure the quality of generated captions by comparing them to the manually provided captions using the CIDEr metric, which ranges from 0 (unrelated to the reference captions) to 10 (perfectly matching the reference captions). When comparing pairs of models, we observed strong correlations between the differences in the CIDEr scores of the model outputs, and side-by-side human evaluations comparing the model outputs. , making XM3600 is a reliable tool for high-quality automatic comparisons between image captioning models on a wide variety of languages beyond English.

Language Selection
We chose 30 languages beyond English, roughly based on their percentage of web content. In addition, we chose an additional five languages that include under-resourced languages that have many native speakers or major native languages from continents that would not be covered otherwise. Finally, we also included English as a baseline, thus resulting in a total of 36 languages, as listed in the table below.

Arabic     Bengali*     Chinese     Croatian     Cusco
Quechua* 		    Czech
Danish     Dutch     English     Filipino     Finnish     French
German     Greek     Hebrew     Hindi     Hungarian     Indonesian
Italian     Japanese     Korean     Maori*     Norwegian     Persian
Polish     Portuguese     Romanian     Russian     Spanish     Swahili*
Swedish     Telugu*     Thai     Turkish     Ukrainian     Vietnamese
List of languages used in XM3600.   *Low-resource languages with many native speakers, or major native languages from continents that would not be covered otherwise.

Image Selection
The images were selected from among those in the Open Images dataset that have location metadata. Since there are many regions where more than one language is spoken, and some areas are not well covered by these images, we designed an algorithm to maximize the correspondence between selected images and the regions where the targeted languages are spoken. The algorithm starts with the selection of images with geo-data corresponding to the languages for which we have the smallest pool (e.g., Persian) and processes them in increasing order of their candidate image pool size. If there aren't enough images in an area where a language is spoken, then we gradually expand the geographic selection radius to: (i) a country where the language is spoken; (ii) a continent where the language is spoken; and, as last resort, (iii) from anywhere in the world. This strategy succeeded in providing our target number of 100 images from an appropriate region for most of the 36 languages, except for Persian (where 14 continent-level images are used) and Hindi (where all 100 images are at the global level, because the in-region images were assigned to Bengali and Telugu).

English

Photo by Chris Sampson		    Swahili

Photo by Henrik Palm		    Telugu

Photo by rojypala



Cusco Quechua

Photo by McKay Savage		    Filipino

Photo by Simon Schoeters		    Chinese

Photo by Stefan Krasowski
Sample images showcasing the geographical diversity of the annotated images. Images used under CC BY 2.0 license.

Caption Generation
In total, all 3600 images (100 images per language) are annotated in all 36 languages, each with an average of two annotations per language, yielding a total of 261,375 captions.

Annotators work in batches of 15 images. The first screen shows all 15 images with their captions in English as generated by a captioning model trained to output a consistent style of the form "<main salient objects> doing <activities> in the <environment>", often with object attributes, such as a "smiling" person, "red" car, etc. The annotators are asked to rate the caption quality given guidelines for a 4-point scale from "excellent" to "bad", plus an option for "not_enough_information". This step forces the annotators to carefully assess caption quality and it primes them to internalize the style of the captions. The following screens show the images again but individually and without the English captions, and the annotators are asked to produce descriptive captions in the target language for each image.

The image batch size of 15 was chosen so that the annotators would internalize the style without remembering the exact captions. Thus, we expect the raters to generate captions based on the image content only and lacking translation artifacts. For example in the example shown below, the Spanish caption mentions “number 42” and the Thai caption mentions “convertibles”, none of which are mentioned in the English captions. The annotators were also provided with a protocol to use when creating the captions, thus achieving style consistency across languages.


Photo by Brian Solis		     English     • A vintage sports car in a showroom with many other vintage sports cars
• The branded classic cars in a row at display
   
Spanish     • Automóvil clásico deportivo en exhibición de automóviles de galería — (Classic sports car in gallery car show)
• Coche pequeño de carreras color plateado con el número 42 en una exhibición de coches — (Small silver racing car with the number 42 at a car show)
   
Thai     • รถเปิดประทุนหลายสีจอดเรียงกันในที่จัดแสดง — (Multicolored convertibles line up in the exhibit)
• รถแข่งวินเทจจอดเรียงกันหลายคันในงานจัดแสดง — (Several vintage racing cars line up at the show.)
Sample captions in three different languages (out of 36 — see full list of captions in Appendix A of the Crossmodal-3600 paper), showcasing the creation of annotations that are consistent in style across languages, while being free of direct-translation artifacts (e.g., the Spanish “number 42” or the Thai “convertibles” would not be possible when directly translating from the English versions). Image used under CC BY 2.0 license.

Caption Quality and Statistics
We ran two to five pilot studies per language to troubleshoot the caption generation process and to ensure high quality captions. We then manually evaluated a random subset of captions. First we randomly selected a sample of 600 images. Then, to measure the quality of captions in a particular language, for each image, we selected for evaluation one of the manually generated captions. We found that:

  • For 25 out of 36 languages, the percentage of captions rated as “Good” or “Excellent” is above 90%, and the rest are all above 70%.
  • For 26 out of 36 languages, the percentage of captions rated as “Bad” is below 2%, and the rest are all below 5%.

For languages that use spaces to separate words, the number of words per caption can be as low as 5 or 6 for some agglutinative languages like Cusco Quechua and Czech, and as high as 18 for an analytic language like Vietnamese. The number of characters per caption also varies drastically — from mid-20s for Korean to mid-90s for Indonesian — depending on the alphabet and the script of the language.

Empirical Evaluation and Results
We empirically measured the ability of the XM3600 annotations to rank image captioning model variations by training four variations of a multilingual image captioning model and comparing the CIDEr differences of the models’ outputs over the XM3600 dataset for 30+ languages, to side-by-side human evaluations. We observed strong correlations between the CIDEr differences and the human evaluations. These results support the use of the XM3600 references as a means to achieve high-quality automatic comparisons between image captioning models on a wide variety of languages beyond English.

Recent Uses
Recently PaLI used XM3600 to evaluate model performance beyond English for image captioning, image-to-text retrieval and text-to-image retrieval. The key takeaways they found when evaluating on XM3600 were that multilingual captioning greatly benefits from scaling the PaLI models, especially for low-resource languages.

Acknowledgements
We would like to acknowledge the coauthors of this work: Xi Chen and Radu Soricut.

Source: Google AI Blog


PaLI: Scaling Language-Image Learning in 100+ Languages

Posted by Xi Chen and Xiao Wang, Software Engineers, Google Research

Advanced language models (e.g., GPT, GLaM, PaLM and T5) have demonstrated diverse capabilities and achieved impressive results across tasks and languages by scaling up their number of parameters. Vision-language (VL) models can benefit from similar scaling to address many tasks, such as image captioning, visual question answering (VQA), object recognition, and in-context optical-character-recognition (OCR). Increasing the success rates for these practical tasks is important for everyday interactions and applications. Furthermore, for a truly universal system, vision-language models should be able to operate in many languages, not just one.

In “PaLI: A Jointly-Scaled Multilingual Language-Image Model”, we introduce a unified language-image model trained to perform many tasks and in over 100 languages. These tasks span vision, language, and multimodal image and language applications, such as visual question answering, image captioning, object detection, image classification, OCR, text reasoning, and others. Furthermore, we use a collection of public images that includes automatically collected annotations in 109 languages, which we call the WebLI dataset. The PaLI model pre-trained on WebLI achieves state-of-the-art performance on challenging image and language benchmarks, such as COCO-Captions, CC3M, nocaps, TextCaps, VQAv2, OK-VQA, TextVQA and others. It also outperforms prior models’ multilingual visual captioning and visual question answering benchmarks.

Overview
One goal of this project is to examine how language and vision models interact at scale and specifically the scalability of language-image models. We explore both per-modality scaling and the resulting cross-modal interactions of scaling. We train our largest model to 17 billion (17B) parameters, where the visual component is scaled up to 4B parameters and the language model to 13B. 

The PaLI model architecture is simple, reusable and scalable. It consists of a Transformer encoder that processes the input text, and an auto-regressive Transformer decoder that generates the output text. To process images, the input to the Transformer encoder also includes "visual words" that represent an image processed by a Vision Transformer (ViT). A key component of the PaLI model is reuse, in which we seed the model with weights from previously-trained uni-modal vision and language models, such as mT5-XXL and large ViTs. This reuse not only enables the transfer of capabilities from uni-modal training, but also saves computational cost.

The PaLI model addresses a wide range of tasks in the language-image, language-only and image-only domain using the same API (e.g., visual-question answering, image captioning, scene-text understanding, etc.). The model is trained to support over 100 languages and tuned to perform multilingually for multiple language-image tasks.

Dataset: Language-Image Understanding in 100+ Languages
Scaling studies for deep learning show that larger models require larger datasets to train effectively. To unlock the potential of language-image pretraining, we construct WebLI, a multilingual language-image dataset built from images and text available on the public web.

WebLI scales up the text language from English-only datasets to 109 languages, which enables us to perform downstream tasks in many languages. The data collection process is similar to that employed by other datasets, e.g. ALIGN and LiT, and enabled us to scale the WebLI dataset to 10 billion images and 12 billion alt-texts.

In addition to annotation with web text, we apply the Cloud Vision API to perform OCR on the images, leading to 29 billion image-OCR pairs. We perform near-deduplication of the images against the train, validation and test splits of 68 common vision and vision-language datasets, to avoid leaking data from downstream evaluation tasks, as is standard in the literature. To further improve the data quality, we score image and alt-text pairs based on their cross-modal similarity, and tune the threshold to keep only 10% of the images, for a total of 1 billion images used for training PaLI.

Sampled images from WebLI associated with multilingual alt-text and OCR. The second image is by jopradier (original), used under the CC BY-NC-SA 2.0 license. Remaining images are also used with permission.
Statistics of recognized languages from alt-text and OCR in WebLI.
Image-text pair counts of WebLI and other large-scale vision-language datasets, CLIP, ALIGN and LiT.

Training Large Language-Image Models
Vision-language tasks require different capabilities and sometimes have diverging goals. Some tasks inherently require localization of objects to solve the task accurately, whereas some other tasks might need a more global view. Similarly, different tasks might require either long or compact answers. To address all of these objectives, we leverage the richness of the WebLI pre-training data and introduce a mixture of pre-training tasks, which prepare the model for a variety of downstream applications. To accomplish the goal of solving a wide variety of tasks, we enable knowledge-sharing between multiple image and language tasks by casting all tasks into a single generalized API (input: image + text; output: text), which is also shared with the pretraining setup. The objectives used for pre-training are cast into the same API as a weighted mixture aimed at both maintaining the ability of the reused model components and training the model to perform new tasks (e.g., split-captioning for image description, OCR prediction for scene-text comprehension, VQG and VQA prediction).

The model is trained in JAX with Flax using the open-sourced T5X and Flaxformer framework. For the visual component, we introduce and train a large ViT architecture, named ViT-e, with 4B parameters using the open-sourced BigVision framework. ViT-e follows the same recipe as the ViT-G architecture (which has 2B parameters). For the language component, we concatenate the dense token embeddings with the patch embeddings produced by the visual component, together as the input to the multimodal encoder-decoder, which is initialized from mT5-XXL. During the training of PaLI, the weights of this visual component are frozen, and only the weights of the multimodal encoder-decoder are updated.

Results
We compare PaLI on common vision-language benchmarks that are varied and challenging. The PaLI model achieves state-of-the-art results on these tasks, even outperforming very large models in the literature. For example, it outperforms the Flamingo model, which is several times larger (80B parameters), on several VQA and image-captioning tasks, and it also sustains performance on challenging language-only and vision-only tasks, which were not the main training objective.

PaLI (17B parameters) outperforms the state-of-the-art approaches (including SimVLM, CoCa, GIT2, Flamingo, BEiT3) on multiple vision-and-language tasks. In this plot we show the absolute score differences compared with the previous best model to highlight the relative improvements of PaLI. Comparison is on the official test splits when available. CIDEr score is used for evaluation of the image captioning tasks, whereas VQA tasks are evaluated by VQA Accuracy.

Model Scaling Results
We examine how the image and language model components interact with each other with regards to model scaling and where the model yields the most gains. We conclude that scaling both components jointly results in the best performance, and specifically, scaling the visual component, which requires relatively few parameters, is most essential. Scaling is also critical for better performance across multilingual tasks.

Scaling both the language and the visual components of the PaLI model contribute to improved performance. The plot shows the score differences compared to the PaLI-3B model: CIDEr score is used for evaluation of the image captioning tasks, whereas VQA tasks are evaluated by VQA Accuracy.
Multilingual captioning greatly benefits from scaling the PaLI models. We evaluate PaLI on a 35-language benchmark Crossmodal-3600. Here we present the average score over all 35 languages and the individual score for seven diverse languages.

Model Introspection: Model Fairness, Biases, and Other Potential Issues
To avoid creating or reinforcing unfair bias within large language and image models, important first steps are to (1) be transparent about the data that were used and how the model used those data, and (2) test for model fairness and conduct responsible data analyses. To address (1), our paper includes a data card and model card. To address (2), the paper includes results of demographic analyses of the dataset. We consider this a first step and know that it will be important to continue to measure and mitigate potential biases as we apply our model to new tasks, in alignment with our AI Principles.

Conclusion
We presented PaLI, a scalable multi-modal and multilingual model designed for solving a variety of vision-language tasks. We demonstrate improved performance across visual-, language- and vision-language tasks. Our work illustrates the importance of scale in both the visual and language parts of the model and the interplay between the two. We see that accomplishing vision and language tasks, especially in multiple languages, actually requires large scale models and data, and will potentially benefit from further scaling. We hope this work inspires further research in multi-modal and multilingual models.

Acknowledgements
We thank all the authors who conducted this research Soravit (Beer) Changpinyo, AJ Piergiovanni, Piotr Padlewski, Daniel Salz, Sebastian Goodman, Adam Grycner, Basil Mustafa, Lucas Beyer, Alexander Kolesnikov, Joan Puigcerver, Nan Ding, Keran Rong, Hassan Akbari,Gaurav Mishra, Linting Xue, Ashish Thapliyal, James Bradbury, Weicheng Kuo, Mojtaba Seyedhosseini, Chao Jia, Burcu Karagol Ayan, Carlos Riquelme, Andreas Steiner, Anelia Angelova, Xiaohua Zhai, Neil Houlsby, Radu Soricut. We also thank Claire Cui, Slav Petrov, Tania Bedrax-Weiss, Joelle Barral, Tom Duerig, Paul Natsev, Fernando Pereira, Jeff Dean, Jeremiah Harmsen, Zoubin Ghahramani, Erica Moreira, Victor Gomes, Sarah Laszlo, Kathy Meier-Hellstern, Susanna Ricco, Rich Lee, Austin Tarango, Emily Denton, Bo Pang, Wei Li, Jihyung Kil, Tomer Levinboim, Julien Amelot, Zhenhai Zhu, Xiangning Chen, Liang Chen, Filip Pavetic, Daniel Keysers, Matthias Minderer, Josip Djolonga, Ibrahim Alabdulmohsin, Mostafa Dehghani, Yi Tay, Elizabeth Adkison, James Cockerille, Eric Ni, Anna Davies, and Maysam Moussalem for their suggestions, improvements and support. We thank Tom Small for providing visualizations for the blogpost.

Source: Google AI Blog


Training Generalist Agents with Multi-Game Decision Transformers

Posted by Winnie Xu, Student Researcher and Kuang-Huei Lee, Software Engineer, Google Research, Brain Team

Current deep reinforcement learning (RL) methods can train specialist artificial agents that excel at decision-making on various individual tasks in specific environments, such as Go or StarCraft. However, little progress has been made to extend these results to generalist agents that would not only be capable of performing many different tasks, but also upon a variety of environments with potentially distinct embodiments.

Looking across recent progress in the fields of natural language processing, vision, and generative models (such as PaLM, Imagen, and Flamingo), we see that breakthroughs in making general-purpose models are often achieved by scaling up Transformer-based models and training them on large and semantically diverse datasets. It is natural to wonder, can a similar strategy be used in building generalist agents for sequential decision making? Can such models also enable fast adaptation to new tasks, similar to PaLM and Flamingo?

As an initial step to answer these questions, in our recent paper “Multi-Game Decision Transformers” we explore how to build a generalist agent to play many video games simultaneously. Our model trains an agent that can play 41 Atari games simultaneously at close-to-human performance and that can also be quickly adapted to new games via fine-tuning. This approach significantly improves upon the few existing alternatives to learning multi-game agents, such as temporal difference (TD) learning or behavioral cloning (BC).

A Multi-Game Decision Transformer (MGDT) can play multiple games at desired level of competency from training on a range of trajectories spanning all levels of expertise.

Don’t Optimize for Return, Just Ask for Optimality
In reinforcement learning, reward refers to the incentive signals that are relevant to completing a task, and return refers to cumulative rewards in a course of interactions between an agent and its surrounding environment. Traditional deep reinforcement learning agents (DQN, SimPLe, Dreamer, etc) are trained to optimize decisions to achieve the optimal return. At every time step, an agent observes the environment (some also consider the interactions that happened in the past) and decides what action to take to help itself achieve a higher return magnitude in future interactions.

In this work, we use Decision Transformers as our backbone approach to training an RL agent. A Decision Transformer is a sequence model that predicts future actions by considering past interactions between an agent and the surrounding environment, and (most importantly) a desired return to be achieved in future interactions. Instead of learning a policy to achieve high return magnitude as in traditional reinforcement learning, Decision Transformers map diverse experiences, ranging from expert-level to beginner-level, to their corresponding return magnitude during training. The idea is that training an agent on a range of experiences (from beginner to expert level) exposes the model to a wider range of variations in gameplay, which in turn helps it extract useful rules of gameplay that allow it to succeed under any circumstance. So during inference, the Decision Transformer can achieve any return value in the range it has seen during training, including the optimal return.

But, how do you know if a return is both optimal and stably achievable in a given environment? Previous applications of Decision Transformers relied on customized definitions of the desired return for each individual task, which required manually defining a plausible and informative range of scalar values that are appropriately interpretable signals for each specific game — a task that is non-trivial and rather unscalable. To address this issue, we instead model a distribution of return magnitudes based on past interactions with the environment during training. At inference time, we simply add an optimality bias that increases the probability of generating actions that are associated with higher returns.

To more comprehensively capture spatial-temporal patterns of agent-environment interactions, we also modified the Decision Transformer architecture to consider image patches instead of a global image representation. Patches allow the model to focus on local dynamics, which helps model game specific information in further detail.

These pieces together give us the backbone of Multi-Game Decision Transformers:

Each observation image is divided into a set of M patches of pixels which are denoted O. Return R, action a, and reward r follows these image patches in each input casual sequence. A Decision Transformer is trained to predict the next input (except for the image patches) to establish causality.

Training a Multi-Game Decision Transformer to Play 41 Games at Once
We train one Decision Transformer agent on a large (~1B) and broad set of gameplay experiences from 41 Atari games. In our experiments, this agent, which we call the Multi-Game Decision Transformer (MGDT), clearly outperforms existing reinforcement learning and behavioral cloning methods — by almost 2 times — on learning to play 41 games simultaneously and performs near human-level competency (100% in the following figure corresponds to the level of human gameplay). These results hold when comparing across training methods in both settings where a policy must be learned from static datasets (offline) as well as those where new data can be gathered from interacting with the environment (online).

Each bar is a combined score across 41 games, where 100% indicates human-level performance. Each blue bar is from a model trained on 41 games simultaneously, whereas each gray bar is from 41 specialist agents. Multi-Game Decision Transformer achieves human-level performance, significantly better than other multi-game agents, even comparable to specialist agents.

This result indicates that Decision Transformers are well-suited for multi-task, multi-environment, and multi-embodiment agents.

A concurrent work, “A Generalist Agent”, shows a similar result, demonstrating that large transformer-based sequence models can memorize expert behaviors very well across many more environments. In addition, their work and our work have nicely complementary findings: They show it’s possible to train across a wide range of environments beyond Atari games, while we show it’s possible and useful to train across a wide range of experiences.

In addition to the performance shown above, empirically we found that MGDT trained on a wide variety of experience is better than MDGT trained only on expert-level demonstrations or simply cloning demonstration behaviors.

Scaling Up Multi-Game Model Size to Achieve Better Performance
Argurably, scale has become the main driving force in many recent machine learning breakthroughs, and it is usually achieved by increasing the number of parameters in a transformer-based model. Our observation on Multi-Game Decision Transformers is similar: the performance increases predictably with larger model size. In particular, its performance appears to have not yet hit a ceiling, and compared to other learning systems performance gains are more significant with increases in model size.

Performance of Multi-Game Decision Transformer (shown by the blue line) increases predictably with larger model size, whereas other models do not.

Pre-trained Multi-Game Decision Transformers Are Fast Learners
Another benefit of MGDTs is that they can learn how to play a new game from very few gameplay demonstrations (which don’t need to all be expert-level). In that sense, MGDTs can be considered pre-trained models capable of being fine-tuned rapidly on small new gameplay data. Compared with other popular pre-training methods, it clearly shows consistent advantages in obtaining higher scores.

Multi-Game Decision Transformer pre-training (DT pre-training, shown in light blue) demonstrates consistent advantages over other popular models in adaptation to new tasks.

Where Is the Agent Looking?
In addition to the quantitative evaluation, it’s insightful (and fun) to visualize the agent’s behavior. By probing the attention heads, we find that the MGDT model consistently places weight in its field of view to areas of the observed images that contain meaningful game entities. We visualize the model’s attention when predicting the next action for various games and find it consistently attends to entities such as the agent’s on screen avatar, agent’s free movement space, non-agent objects, and key environment features. For example, in an interactive setting, having an accurate world model requires knowing how and when to focus on known objects (e.g., currently present obstacles) as well as expecting and/or planning over future unknowns (e.g., negative space). This diverse allocation of attention to many key components of each environment ultimately improves performance.

Here we can see the amount of weight the model places on each key asset of the game scene. Brighter red indicates more emphasis on that patch of pixels.

The Future of Large-Scale Generalist Agents
This work is an important step in demonstrating the possibility of training general-purpose agents across many environments, embodiments, and behavior styles. We have shown the benefit of increased scale on performance and the potential with further scaling. These findings seem to point to a generalization narrative similar to other domains like vision and language — we look forward to exploring the great potential of scaling data and learning from diverse experiences.

We look forward to future research towards developing performant agents for multi-environment and multi-embodiment settings. Our code and model checkpoints can soon be accessed here.

Acknowledgements
We’d like to thank all remaining authors of the paper including Igor Mordatch, Ofir Nachum Menjiao Yang, Lisa Lee, Daniel Freeman, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski.

Source: Google AI Blog


Rewriting Image Captions for Visual Question Answering Data Creation

Posted by Soravit Beer Changpinyo and Doron Kukliansky‎, Senior Software Engineers, Google Research

Visual Question Answering (VQA) is a useful machine learning (ML) task that requires a model to answer a visual question about an image. What makes it challenging is its multi-task and open-ended nature; it involves solving multiple technical research questions in computer vision and natural language understanding simultaneously. Yet, progress on this task would enable a wide range of applications, from assisting the blind and the visually-impaired or communicating with robots to enhancing the user’s visual experience with external knowledge.

Effective and robust VQA systems cannot exist without high-quality, semantically and stylistically diverse large-scale training data of image-question-answer triplets. But, creating such data is time consuming and onerous. Perhaps unsurprisingly, the VQA community has focused more on sophisticated model development rather than scalable data creation.

In “All You May Need for VQA are Image Captions,” published at NAACL 2022, we explore VQA data generation by proposing “Visual Question Generation with Question Answering Validation” (VQ2A), a pipeline that works by rewriting a declarative caption into multiple interrogative question-answer pairs. More specifically, we leverage two existing assets — (i) large-scale image-text data and (ii) large-capacity neural text-to-text models — to achieve automatic VQA data generation. As the field has progressed, the research community has been making these assets larger and stronger in isolation (for general purposes such as learning text-only or image-text representations); together, they can achieve more and we adapt them for VQA data creation purposes. We find our approach can generate question-answer pairs with high precision and that this data can successfully be used for training VQA models to improve performance.

The VQ2A technique enables VQA data generation at scale from image captions by rewriting each caption into multiple question-answer pairs.

VQ2A Overview
The first step of the VQ2A approach is to apply heuristics based on named entity recognition, part-of-speech tagging and manually defined rules to generate answer candidates from the image caption. These generated candidates are small pieces of information that may be relevant subjects about which to ask questions. We also add to this list two default answers, “yes” and “no”, which allow us to generate Boolean questions.

Then, we use a T5 model that was fine-tuned to generate questions for the candidate, resulting in [question, candidate answer] pairs. We then filter for the highest quality pairs using another T5 model (fine-tuned to answer questions) by asking it to answer the question based on the caption. was . That is, we compare the candidate answer to the output of this model and if the two answers are similar enough, we define this question as high quality and keep it. Otherwise, we filter it out.

The idea of using both question answering and question generation models to check each other for their round-trip consistency has been previously explored in other contexts. For instance, Q2 uses this idea to evaluate factual consistency in knowledge-grounded dialogues. In the end, the VQ2A approach, as illustrated below, can generate a large number of [image, question, answer] triplets that are high-quality enough to be used as VQA training data.

VQ2A consists of three main steps: (i) candidate answer extraction, (ii) question generation, (iii) question answering and answer validation.

Results
Two examples of our generated VQA data are shown below, one based on human-written COCO Captions (COCO) and the other on automatically-collected Conceptual Captions (CC3M), which we call VQ2A-COCO and VQ2A-CC3M, respectively. We highlight the variety of question types and styles, which are critical for VQA. Overall, the cleaner the captions (i.e., the more closely related they are to their paired image), the more accurate the generated triplets. Based on 800 samples each, 87.3% of VQ2A-COCO and 66.0% VQ2A-CC3M are found by human raters to be valid, suggesting that our approach can generate question-answer pairs with high precision.

Generated question-answer pairs based on COCO Captions (top) and Conceptual Captions (bottom). Grey highlighting denotes questions that do not appear in VQAv2, while green highlighting denotes those that do, indicating that our approach is capable of generating novel questions that an existing VQA dataset does not have.

Finally, we evaluate our generated data by using it to train VQA models (highlights shown below). We observe that our automatically-generated VQA data is competitive with manually-annotated target VQA data. First, our VQA models achieve high performance on target benchmarks “out-of-the-box”, when trained only on our generated data (light blue and light red vs. yellow). Once fine-tuned on target data, our VQA models outperform target-only training slightly on large-scale benchmarks like VQAv2 and GQA, but significantly on the small, knowledge-seeking OK-VQA (dark blue/red vs. light blue/red).

VQA accuracy on popular benchmark datasets.

Conclusion
All we may need for VQA are image captions! This work demonstrates that it is possible to automatically generate high-quality VQA data at scale, serving as an essential building block for VQA and vision-and-language models in general (e.g., ALIGN, CoCa). We hope that our work inspires other work on data-centric VQA.

Acknowledgments
We thank Roee Aharoni, Idan Szpektor, and Radu Soricut for their feedback on this blogpost. We also thank our co-authors: Xi Chen, Nan Ding, Idan Szpektor, and Radu Soricut. We acknowledge contributions from Or Honovich, Hagai Taitelbaum, Roee Aharoni, Sebastian Goodman, Piyush Sharma, Nassim Oufattole, Gal Elidan, Sasha Goldshtein, and Avinatan Hassidim. Finally, we thank the authors of Q2, whose pipeline strongly influences this work.

Source: Google AI Blog


Rewriting Image Captions for Visual Question Answering Data Creation

Posted by Soravit Beer Changpinyo and Doron Kukliansky‎, Senior Software Engineers, Google Research

Visual Question Answering (VQA) is a useful machine learning (ML) task that requires a model to answer a visual question about an image. What makes it challenging is its multi-task and open-ended nature; it involves solving multiple technical research questions in computer vision and natural language understanding simultaneously. Yet, progress on this task would enable a wide range of applications, from assisting the blind and the visually-impaired or communicating with robots to enhancing the user’s visual experience with external knowledge.

Effective and robust VQA systems cannot exist without high-quality, semantically and stylistically diverse large-scale training data of image-question-answer triplets. But, creating such data is time consuming and onerous. Perhaps unsurprisingly, the VQA community has focused more on sophisticated model development rather than scalable data creation.

In “All You May Need for VQA are Image Captions,” published at NAACL 2022, we explore VQA data generation by proposing “Visual Question Generation with Question Answering Validation” (VQ2A), a pipeline that works by rewriting a declarative caption into multiple interrogative question-answer pairs. More specifically, we leverage two existing assets — (i) large-scale image-text data and (ii) large-capacity neural text-to-text models — to achieve automatic VQA data generation. As the field has progressed, the research community has been making these assets larger and stronger in isolation (for general purposes such as learning text-only or image-text representations); together, they can achieve more and we adapt them for VQA data creation purposes. We find our approach can generate question-answer pairs with high precision and that this data can successfully be used for training VQA models to improve performance.

The VQ2A technique enables VQA data generation at scale from image captions by rewriting each caption into multiple question-answer pairs.

VQ2A Overview
The first step of the VQ2A approach is to apply heuristics based on named entity recognition, part-of-speech tagging and manually defined rules to generate answer candidates from the image caption. These generated candidates are small pieces of information that may be relevant subjects about which to ask questions. We also add to this list two default answers, “yes” and “no”, which allow us to generate Boolean questions.

Then, we use a T5 model that was fine-tuned to generate questions for the candidate, resulting in [question, candidate answer] pairs. We then filter for the highest quality pairs using another T5 model (fine-tuned to answer questions) by asking it to answer the question based on the caption. was . That is, we compare the candidate answer to the output of this model and if the two answers are similar enough, we define this question as high quality and keep it. Otherwise, we filter it out.

The idea of using both question answering and question generation models to check each other for their round-trip consistency has been previously explored in other contexts. For instance, Q2 uses this idea to evaluate factual consistency in knowledge-grounded dialogues. In the end, the VQ2A approach, as illustrated below, can generate a large number of [image, question, answer] triplets that are high-quality enough to be used as VQA training data.

VQ2A consists of three main steps: (i) candidate answer extraction, (ii) question generation, (iii) question answering and answer validation.

Results
Two examples of our generated VQA data are shown below, one based on human-written COCO Captions (COCO) and the other on automatically-collected Conceptual Captions (CC3M), which we call VQ2A-COCO and VQ2A-CC3M, respectively. We highlight the variety of question types and styles, which are critical for VQA. Overall, the cleaner the captions (i.e., the more closely related they are to their paired image), the more accurate the generated triplets. Based on 800 samples each, 87.3% of VQ2A-COCO and 66.0% VQ2A-CC3M are found by human raters to be valid, suggesting that our approach can generate question-answer pairs with high precision.

Generated question-answer pairs based on COCO Captions (top) and Conceptual Captions (bottom). Grey highlighting denotes questions that do not appear in VQAv2, while green highlighting denotes those that do, indicating that our approach is capable of generating novel questions that an existing VQA dataset does not have.

Finally, we evaluate our generated data by using it to train VQA models (highlights shown below). We observe that our automatically-generated VQA data is competitive with manually-annotated target VQA data. First, our VQA models achieve high performance on target benchmarks “out-of-the-box”, when trained only on our generated data (light blue and light red vs. yellow). Once fine-tuned on target data, our VQA models outperform target-only training slightly on large-scale benchmarks like VQAv2 and GQA, but significantly on the small, knowledge-seeking OK-VQA (dark blue/red vs. light blue/red).

VQA accuracy on popular benchmark datasets.

Conclusion
All we may need for VQA are image captions! This work demonstrates that it is possible to automatically generate high-quality VQA data at scale, serving as an essential building block for VQA and vision-and-language models in general (e.g., ALIGN, CoCa). We hope that our work inspires other work on data-centric VQA.

Acknowledgments
We thank Roee Aharoni, Idan Szpektor, and Radu Soricut for their feedback on this blogpost. We also thank our co-authors: Xi Chen, Nan Ding, Idan Szpektor, and Radu Soricut. We acknowledge contributions from Or Honovich, Hagai Taitelbaum, Roee Aharoni, Sebastian Goodman, Piyush Sharma, Nassim Oufattole, Gal Elidan, Sasha Goldshtein, and Avinatan Hassidim. Finally, we thank the authors of Q2, whose pipeline strongly influences this work.

Source: Google AI Blog