New AutoSkill framework optimizes frame selection for long-video QA

Researchers have developed AutoSkill, a novel framework designed to automatically discover and route frame-selection skills for long-video question answering. This method addresses the limitation of existing approaches that use a single frame-selection strategy for all questions, by demonstrating that different question types benefit from distinct strategies. AutoSkill iteratively proposes, evaluates, and refines skills using LLM agents and a taxonomy derived from unlabelled data, improving the performance of models like Qwen2.5-VL-7B and Qwen3.5-4B. AI

IMPACT This research could lead to more efficient and accurate AI systems for understanding and querying long video content.

RANK_REASON Academic paper detailing a new framework for video question answering. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

• alphaXiv
• arXiv
• AutoSkill
• CatalyzeX
• DagsHub
• Gotit.pub
• Hugging Face
• Qwen2.5-VL-7B
• Qwen3.5 4B
• ScienceCast

• paper
• model release

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