When Trackers Fail: VLM-Guided Verification and Recovery for Robust Video Object Segmentation
Recent trackers built on foundation models such as the Segment Anything Model (SAM) achieve strong performance by propagating object masks through memory. However, this strength is also a key source of failure: under occlusion, reappearance, or distractors, an incorrect prediction can enter the tracking loop and be propagated over time, resulting in persistent identity drift. Existing approaches seek to prevent such failures through improved memory and temporal modeling, but once a failure occurs, the tracker has no explicit mechanism to recognize or correct it. We address this gap with Dual-Agent, a failure-aware tracking framework that complements propagation with explicit verification ...
| Authors | Valay Bundele, Susmit Agrawal, Mehran Hosseinzadeh et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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