QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking
Tracking points in videos is typically formulated as frame-to-frame correspondence, where each point is matched locally to the next frame. While this works over short horizons, errors accumulate under articulation, occlusion, and viewpoint change, leading to silent semantic drift that existing trackers cannot detect or correct. In this work, we revisit long-horizon tracking from a monitoring perspective and introduce QueST, a monitoring-by-design framework that treats interaction-relevant entities as persistent semantic queries rather than transient point tracks. Instead of local propagation, each query attends globally over spatio-temporal video features at every time-step, providing a s...
| Authors | Mayank Anand, Mohammad Saqlain, Kyan Mahajan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 May 2026 |
| Last updated at source | 10 May 2026 |
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