Methods › Computer Vision › Video Object Segmentation Models › State-Aware Tracker
State-Aware Tracker
Introduced by Xi Chen et al. in State-Aware Tracker for Real-Time Video Object Segmentation
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
State-Aware Tracker is a pipeline for semi-supervised video object segmentation. It takes each target object as a tracklet, which not only makes the pipeline more efficient but also filters distractors to facilitate target modeling. For more stable and robust performance over video sequences, SAT gets awareness for each state and makes self-adaptation via two feedback loops. One loop assists SAT in generating more stable tracklets. The other loop helps to construct a more robust and holistic target representation.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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State-Aware Tracker for Real-Time Video Object Segmentation 1 Mar 2020 · 1 repository · arXiv:2003.00482Syntology ran 1 of 7 samples · 6 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Segmentation | 1 |
| Semantic Segmentation | 1 |
| Semi-Supervised Video Object Segmentation | 1 |
| Video Object Segmentation | 1 |
| Video Semantic Segmentation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections