arXiv:2607.00446v1 Announce Type: cross Abstract: As video corpora continue to expand in both scale and task complexity, there is increasing demand for approaches that retrieve relevant videos from large-scale corpora (inter-video reasoning) and subsequently perform fine-grained, query-conditioned tasks (intra-video reasoning) within the retrieved content, such as temporal grounding. However, existing approaches typically treat retrieval as a preprocessing step, and consequently, when the initial retrieval fails, there is no mechanism to refine the search, leading to the failure of subsequent
This development addresses a critical limitation in existing video retrieval systems as video corpora rapidly expand, pushing the need for more sophisticated and iterative search mechanisms.
Improving video retrieval and intra-video reasoning has significant implications for training large AI models, enhancing autonomous systems' perception, and enabling more effective analysis of visual data.
Current video retrieval, often a one-shot process, is evolving into an iterative and refined search, allowing for more precise information extraction and reduced reliance on initial query accuracy.
- · AI development platforms
- · Video analytics companies
- · Autonomous vehicle developers
- · Content management systems
- · Legacy video search engines
- · Systems highly dependent on perfect initial queries
More accurate and efficient retrieval of specific information from vast video datasets.
Accelerated development and training of advanced AI models across various domains, including robotics and surveillance.
Potential for new video-centric AI agent applications that can autonomously learn and act based on visual context.
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Read at arXiv cs.AI