arXiv:2607.02551v1 Announce Type: cross Abstract: 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 t
The continuous improvement in video multimodal large language models necessitates addressing existing limitations in fine-grained spatiotemporal perception to push the boundaries of AI capabilities.
Improving fine-grained spatiotemporal perception is critical for AI agents and automated systems that require precise understanding of subtle changes in dynamic environments.
The ability of AI models to detect and provide evidence for small, local changes within video content, moving beyond global semantics.
- · AI developers
- · Robotics
- · Surveillance technology
- · Generative AI
- · Legacy video analysis systems
Increased accuracy and reliability of AI systems in tasks requiring detailed video analysis.
Accelerated development of more sophisticated AI agents capable of nuanced environmental interaction and response.
Enhanced automation in fields ranging from quality control to security, where subtle visual detection is paramount.
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Read at arXiv cs.AI