Papers › Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Fast Video Object Segmentation With Temporal Aggregation Network and Dynamic Template Matching
Xuhua Huang, Jiarui Xu, Yu-Wing Tai, Chi-Keung Tang
Significant progress has been made in Video Object Segmentation (VOS), the video object tracking task in its finest level. While the VOS task can be naturally decoupled into image semantic segmentation and video object tracking, significantly much more research effort has been made in segmentation than tracking. In this paper, we introduce "tracking-by-detection" into VOS which can coherently integrate segmentation into tracking, by proposing a new temporal aggregation network and a novel dynamic time-evolving template matching mechanism to achieve significantly improved performance. Notably, our method is entirely online and thus suitable for one-shot learning, and our end-to-end trainable model allows multiple object segmentation in one forward pass. We achieve new state-of-the-art performance on the DAVIS benchmark without complicated bells and whistles in both speed and accuracy, with a speed of 0.14 second per frame and J&F measure of 75.9% respectively.
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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 | DAVIS 2016 | RGMP (val) | F-measure (Mean) | 68.9 | #71 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | RGMP (val) | J&F | 68.8 | #71 of 78 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | DAVIS 2016 | RGMP (val) | Jaccard (Mean) | 68.6 | #71 of 78 | 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.
Methods
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