Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding

• URL: http://arxiv.org/abs/2512.09354v1
• Date: Wed, 10 Dec 2025 06:28:00 GMT
• Title: Video-QTR: Query-Driven Temporal Reasoning Framework for Lightweight Video Understanding
• Authors: Xinkui Zhao, Zuxin Wang, Yifan Zhang, Guanjie Cheng, Yueshen Xu, Shuiguang Deng, Chang Liu, Naibo Wang, Jianwei Yin,
• Abstract summary: Video-QTR is a lightweight framework that redefines video comprehension as a query-guided reasoning process.<n>We show that Video-QTR achieves state-of-the-art performance while reducing input frame consumption by up to 73%.
• Score: 37.682165829414494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of multimodal large-language models (MLLMs) has significantly expanded the scope of visual language reasoning, enabling unified systems to interpret and describe complex visual content. However, applying these models to long-video understanding remains computationally intensive. Dense frame encoding generates excessive visual tokens, leading to high memory consumption, redundant computation, and limited scalability in real-world applications. This inefficiency highlights a key limitation of the traditional process-then-reason paradigm, which analyzes visual streams exhaustively before semantic reasoning. To address this challenge, we introduce Video-QTR (Query-Driven Temporal Reasoning), a lightweight framework that redefines video comprehension as a query-guided reasoning process. Instead of encoding every frame, Video-QTR dynamically allocates perceptual resources based on the semantic intent of the query, creating an adaptive feedback loop between reasoning and perception. Extensive experiments across five benchmarks: MSVD-QA, Activity Net-QA, Movie Chat, and Video MME demonstrate that Video-QTR achieves state-of-the-art performance while reducing input frame consumption by up to 73%. These results confirm that query-driven temporal reasoning provides an efficient and scalable solution for video understanding.

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