DELTAVID: Enhancing Fine-Grained Spatiotemporal Perception with Cross-Video Differences
Video multimodal large language models have made strong progress on open-ended video understanding, but they still lack precise local spatiotemporal perception. When two videos share almost the same global semantics and differ only in a short time span or a small region, current models often fail to find the change and provide reliable evidence. We propose DELTAVID, a verifiable proxy-task framework that enhances fine-grained spatiotemporal perception with cross-video differences. The key idea is to turn cross-video spot-the-difference into a trainable perception signal, where a model identifies local changes, judges temporal boundaries, and organizes spatial evidence by comparing similar...
| Authors | Yankai Yang, Yancheng Long, Bin Wen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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