arXiv:2605.23826v1 Announce Type: cross Abstract: Keyframe selection is a direct way to provide verifiable visual evidence for long-video question answering (QA). Queries differ in what they require, and finding the right frames depends on knowing what to look for. Existing keyframe selectors either score every frame against a single query, or decompose the query into a fixed schema evaluated by a single visual tool. We propose ToolMerge, a keyframe retrieval method based on decomposition and merging: an Large Language Model (LLM) based planner decomposes the query into tool calls and specifie
The proliferation of long-form video content and the increasing sophistication of AI models, particularly LLMs, make this an opportune time for developing advanced video retrieval techniques.
This development enhances the ability to quickly and accurately extract specific information from extensive video datasets, crucial for various applications from security to content creation and analysis.
Keyframe selection methods are shifting from single-query, monolithic approaches to more flexible, LLM-driven decomposition and merging of queries, significantly improving retrieval accuracy and relevance.
- · AI developers
- · Video analytics companies
- · Security and intelligence agencies
- · Content creators and platforms
- · Manual video review processes
- · Inefficient video search tools
Improved query resolution directly leads to more efficient and accurate extraction of visual evidence from long videos.
This efficiency can drive new applications in automated content moderation, enhanced surveillance, and more precise data analysis from video streams.
The broader adoption of such systems could accelerate the development of truly autonomous AI agents capable of complex visual reasoning and information synthesis.
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Read at arXiv cs.CL