See More, Think Deeper: Query-Expanded Visual Evidence and Answer-Clue Guided Reflection for Long Video Understanding
arXiv:2606.09064v1 Announce Type: cross Abstract: Recent advances in Video Large Language Models (Video-LLMs) have enabled performance on long-video understanding tasks. However, existing methods still face two key limitations: evidence acquisition often relies on a single search intent, and answer generation lacks an effective visual feedback mechanism. To address these limitations, we propose \textbf{CoVER}, a Comprehensive Visual Evidence and Reflection framework for long-video understanding. CoVER enables Video-LLMs to \textbf{See More} by dynamically gathering query-expanded visual eviden
The rapid development of large language models is continually pushing the boundaries of multimodal understanding, with long-video comprehension being a current frontier where previous methods have shown limitations.
Improved long-video understanding enables more sophisticated AI agents and automation across various industries, creating new analytical capabilities from extensive visual data.
AI systems can now process and interpret long-form video content with greater accuracy and depth, moving beyond singular query interpretations to a more comprehensive analysis.
- · AI development firms
- · Video analytics industry
- · Surveillance and security sectors
- · Media and entertainment AI tools
- · Manual video analysis services
- · AI solutions with limited video processing capabilities
Enhanced capabilities for AI to interpret complex, time-series visual data sets will emerge.
This could lead to accelerated development of fully autonomous agents that derive insights from continuous visual inputs.
These advanced agents may eventually automate complex tasks requiring continuous visual situational awareness, contributing to broader AI agent proliferation.
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Read at arXiv cs.AI