Papers › Video Instance Segmentation with a Propose-Reduce Paradigm
Video Instance Segmentation with a Propose-Reduce Paradigm
Huaijia Lin, Ruizheng Wu, Shu Liu, Jiangbo Lu, Jiaya Jia
Video instance segmentation (VIS) aims to segment and associate all instances of predefined classes for each frame in videos. Prior methods usually obtain segmentation for a frame or clip first, and merge the incomplete results by tracking or matching. These methods may cause error accumulation in the merging step. Contrarily, we propose a new paradigm -- Propose-Reduce, to generate complete sequences for input videos by a single step. We further build a sequence propagation head on the existing image-level instance segmentation network for long-term propagation. To ensure robustness and high recall of our proposed framework, multiple sequences are proposed where redundant sequences of the same instance are reduced. We achieve state-of-the-art performance on two representative benchmark datasets -- we obtain 47.6% in terms of AP on YouTube-VIS validation set and 70.4% for J&F on DAVIS-UVOS validation set. Code is available at https://github.com/dvlab-research/ProposeReduce.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Video Object Segmentation | DAVIS 2017 (val) | Propose-Reduce | F-measure (Mean) | 73.8 | #2 of 10 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2017 (val) | Propose-Reduce | J&F | 70.4 | #2 of 10 | Archive leaderboard | report |
| Unsupervised Video Object Segmentation | DAVIS 2017 (val) | Propose-Reduce | Jaccard (Mean) | 67.0 | #2 of 10 | 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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