Tag Archives: ICCV

Announcing the YouTube-8M Segments Dataset



Over the last two years, the First and Second YouTube-8M Large-Scale Video Understanding Challenge and Workshop have collectively drawn 1000+ teams from 60+ countries to further advance large-scale video understanding research. While these events have enabled great progress in video classification, the YouTube dataset on which they were based only used machine-generated video-level labels, and lacked fine-grained temporally localized information, which limited the ability of machine learning models to predict video content.

To accelerate the research of temporal concept localization, we are excited to announce the release of YouTube-8M Segments, a new extension of the YouTube-8M dataset that includes human-verified labels at the 5-second segment level on a subset of YouTube-8M videos. With the additional temporal annotations, YouTube-8M is now both a large-scale classification dataset as well as a temporal localization dataset. In addition, we are hosting another Kaggle video understanding challenge focused on temporal localization, as well as an affiliated 3rd Workshop on YouTube-8M Large-Scale Video Understanding at the 2019 International Conference on Computer Vision (ICCV’19).



YouTube-8M Segments
Video segment labels provide a valuable resource for temporal localization not possible with video-level labels, and enable novel applications, such as capturing special video moments. Instead of exhaustively labeling all segments in a video, to create the YouTube-8M Segments extension, we manually labeled 5 segments (on average) per randomly selected video on the YouTube-8M validation dataset, totalling ~237k segments covering 1000 categories.

This dataset, combined with the previous YouTube-8M release containing a very large number of machine generated video-level labels, should allow learning temporal localization models in novel ways. Evaluating such classifiers is of course very challenging if only noisy video-level labels are available. We hope that the newly added human-labeled annotations will help ensure that researchers can more accurately evaluate their algorithms.

The 3rd YouTube-8M Video Understanding Challenge
This year the YouTube-8M Video Understanding Challenge focuses on temporal localization. Participants are encouraged to leverage noisy video-level labels together with a small segment-level validation set in order to better annotate and temporally localize concepts of interest. Unlike last year, there is no model size restriction. Each of the top 10 teams will be awarded $2,500 to support their travel to Seoul to attend ICCV’19. For details, please visit the Kaggle competition page.

The 3rd Workshop on YouTube-8M Large-Scale Video Understanding
Continuing in the tradition of the previous two years, the 3rd workshop will feature four invited talks by distinguished researchers as well as presentations by top-performing challenge participants. We encourage those who wish to attend to submit papers describing their research, experiments, or applications based on the YouTube-8M dataset, including papers summarizing their participation in the challenge above. Please refer to the workshop page for more details.

It is our hope that this newest extension will serve as a unique playground for temporal localization that mimics real world scenarios. We also look forward to the new challenge and workshop, which we believe will continue to advance research in large-scale video understanding. We hope you will join us again!

Acknowledgements
This post reflects the work of many machine perception researchers including Ke Chen, Nisarg Kothari, Joonseok Lee, Hanhan Li, Paul Natsev, Joe Yue-Hei Ng, Naderi Parizi, David Ross, Cordelia Schmid, Javier Snaider, Rahul Sukthankar, George Toderici, Balakrishnan Varadarajan, Sudheendra Vijayanarasimhan, Yexin Wang, Zheng Xu, as well as Julia Elliott and Walter Reade from Kaggle. We are also grateful for the support and advice from our partners at YouTube.

Source: Google AI Blog


Announcing Open Images V5 and the ICCV 2019 Open Images Challenge



In 2016, we introduced Open Images, a collaborative release of ~9 million images annotated with labels spanning thousands of object categories. Since then we have rolled out several updates, culminating with Open Images V4 in 2018. In total, that release included 15.4M bounding-boxes for 600 object categories, making it the largest existing dataset with object location annotations, as well as over 300k visual relationship annotations.

Today we are happy to announce Open Images V5, which adds segmentation masks to the set of annotations, along with the second Open Images Challenge, which will feature a new instance segmentation track based on this data.

Open Images V5
Open Images V5 features segmentation masks for 2.8 million object instances in 350 categories. Unlike bounding-boxes, which only identify regions in which an object is located, segmentation masks mark the outline of objects, characterizing their spatial extent to a much higher level of detail. We have put particular effort into ensuring consistent annotations across different objects (e.g., all cat masks include their tail; bags carried by camels or persons are included in their mask). Importantly, these masks cover a broader range of object categories and a larger total number of instances than any previous dataset.

Example masks on the training set of Open Images V5. These have been produced by our interactive segmentation process. The first example also shows a bounding box, for comparison. From left to right, top to bottom: Tea and cake at the Fitzwilliam Museum by Tim Regan, Pilota II by Euskal kultur erakundea Institut culturel basque, Rheas by Dag Peak, Wuxi science park, 1995 by Gary Stevens, Cat Cafe Shinjuku calico by Ari Helminen, and Untitled by Todd Huffman. All images used under CC BY 2.0 license.
The segmentation masks on the training set (2.68M) have been produced by our state-of-the-art interactive segmentation process, where professional human annotators iteratively correct the output of a segmentation neural network. This is more efficient than manual drawing alone, while at the same time delivering accurate masks (intersection-over-union 84%). Additionally, we release 99k masks on the validation and test sets, which have been annotated manually with a strong focus on quality. These are near-perfect and capture even fine details of complex object boundaries (e.g. spiky flowers and thin structures in man-made objects). Both our training and validation+test annotations offer more accurate object boundaries than the polygon annotations provided by most existing datasets.

Example masks on the validation and test sets of Open Images V5, drawn completely manually. From left to right: thistle flowers by sophie, still life with ax by liz west, Fischkutter KOŁ-180 in Kolobrzeg (PL) by zeesenboot. All images used under CC BY 2.0 license.
In addition to the masks, we also added 6.4M new human-verified image-level labels, reaching a total of 36.5M over nearly 20,000 categories. Finally, we improved annotation density for 600 object categories on the validation and test sets, adding more than 400k bounding boxes to match the density in the training set. This ensures more precise evaluation of object detection models.

Open Images Challenge 2019
In conjunction with this release, we are also introducing the second Open Images Challenge, to be held at the 2019 International Conference on Computer Vision (ICCV 2019). This Challenge will have a new instance segmentation track based on the data above. Moreover, as in the 2018 edition, it will also feature a large-scale object detection track (500 categories with 12.2M training bounding-boxes), and a visual relationship detection track for detecting pairs of objects in particular relations (329 relationship triplets with 375k training samples, e.g., “woman playing guitar” or “beer on table”).

The training set with all annotations is available now. The test set has the same 100k images as the 2018 Challenge and will be launched again on June 3rd, 2019 by Kaggle. The evaluation servers will open on June 3rd for the object detection and visual relationship tracks, and on July 1st for the instance segmentation track. The deadline for submission of results is October 1st, 2019.

We hope that the exceptionally large and diverse training set will inspire research into more advanced instance segmentation models. The extremely accurate ground-truth masks we provide rewards subtle improvements in the output segmentations, and thus will encourage the development of higher-quality models that deliver precise boundaries. Finally, having a single dataset with unified annotations for image classification, object detection, visual relationship detection, and instance segmentation will enable researchers to study these tasks jointly and stimulate progress towards genuine scene understanding.

Source: Google AI Blog