Efficient Spatio-Temporal Grounding with Multimodal Large Models via Second-Level Tracking and RL Verification
Spatio-temporal grounding in long videos requires precise temporal localization and robust object tracking conditioned on natural-language queries. While recent vision-language models (VLMs) show strong reasoning ability, directly applying frame-by-frame inference to long sequences is computationally expensive and unstable. We propose a practical pipeline that shifts from frame-level to second-level tracking and performs cross-second smoothing to preserve continuity while reducing sequence length. To improve reasoning supervision, we synthesize chain-of-thought style trajectories using advanced multimodal models for temporal localization and target selection, and replace generated spatio-...
| Authors | Tianshu Zhang, Yan Wang, Ji Qi et al. |
|---|---|
| Primary category | cs.CV |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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