Papers › YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
YouTube-VOS: Sequence-to-Sequence Video Object Segmentation
Ning Xu, Linjie Yang, Yuchen Fan, Jianchao Yang, Dingcheng Yue, Yuchen Liang, Brian Price, Scott Cohen, Thomas Huang
Learning long-term spatial-temporal features are critical for many video analysis tasks. However, existing video segmentation methods predominantly rely on static image segmentation techniques, and methods capturing temporal dependency for segmentation have to depend on pretrained optical flow models, leading to suboptimal solutions for the problem. End-to-end sequential learning to explore spatial-temporal features for video segmentation is largely limited by the scale of available video segmentation datasets, i.e., even the largest video segmentation dataset only contains 90 short video clips. To solve this problem, we build a new large-scale video object segmentation dataset called YouTube Video Object Segmentation dataset (YouTube-VOS). Our dataset contains 3,252 YouTube video clips and 78 categories including common objects and human activities. This is by far the largest video object segmentation dataset to our knowledge and we have released it at https://youtube-vos.org. Based on this dataset, we propose a novel sequence-to-sequence network to fully exploit long-term spatial-temporal information in videos for segmentation. We demonstrate that our method is able to achieve the best results on our YouTube-VOS test set and comparable results on DAVIS 2016 compared to the current state-of-the-art methods. Experiments show that the large scale dataset is indeed a key factor to the success of our model.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2018 | S2S | F-Measure (Seen) | 70.0 | #48 of 53 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2018 | S2S | F-Measure (Unseen) | 61.2 | #48 of 53 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2018 | S2S | Jaccard (Seen) | 71.0 | #48 of 53 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2018 | S2S | Overall | 64.4 | #48 of 53 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | YouTube-VOS 2018 | S2S | Speed (FPS) | 55.5 | #48 of 53 | Archive leaderboard | report |
| Video Object Segmentation | YouTube-VOS 2018 | S2S (offline) | F-Measure (Unseen) | 50.3 | #17 of 17 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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