GenSpan: Generation-Calibrated Motion Span Priors for Multi-Verb Video Corpus Moment Retrieval
arXiv:2603.22121v2 Announce Type: replace-cross Abstract: Video Corpus Moment Retrieval (VCMR) aims to retrieve both the correct video and its temporal segment corresponding to a natural-language query, a task that is especially challenging for multi-verb queries where temporal action ordering is critical. Existing approaches often rely solely on text or static images and struggle to capture implicit motion dynamics, leading to retrieval errors and temporal misalignment. We propose GenSpan, a generation-calibrated VCMR framework that constructs short auxiliary videos from LLM-selected subtitle
The proliferation of advanced LLMs and the increasing demand for sophisticated video understanding in AI applications drive the development of more nuanced video retrieval methods.
Improving video corpus moment retrieval, especially for complex multi-verb queries, enhances the utility of vast video datasets for training and real-world applications across various sectors.
This research introduces a novel generation-calibrated framework that leverages LLMs to improve the accuracy and temporal precision of video moment retrieval by focusing on motion dynamics.
- · AI researchers and developers
- · Video analytics companies
- · Content management platforms
- · Generative AI startups
- · Legacy video search engines
- · Systems reliant on static image or text-only video understanding
- · Human video annotators for basic tasks
Enhanced ability to precisely locate events within large video datasets, improving data efficiency for AI model training.
Accelerated development of more capable autonomous agents that can interpret complex temporal actions from video footage.
Potentially enables new forms of automated content creation or detailed event reconstruction from vast archives of unstructured video data.
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Read at arXiv cs.AI