Re-thinking Temporal Search for Long-Form Video Understanding

• URL: http://arxiv.org/abs/2504.02259v2
• Date: Sun, 06 Apr 2025 14:10:42 GMT
• Title: Re-thinking Temporal Search for Long-Form Video Understanding
• Authors: Jinhui Ye, Zihan Wang, Haosen Sun, Keshigeyan Chandrasegaran, Zane Durante, Cristobal Eyzaguirre, Yonatan Bisk, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei, Jiajun Wu, Manling Li,
• Abstract summary: Current temporal search methods only achieve 2.1% temporal F1 score on the Longvideobench subset.<n>Inspired by visual search in images, we propose a lightweight temporal search framework, T* that reframes costly temporal search as spatial search.<n>Extensive experiments show that integrating T* with existing methods significantly improves SOTA long-form video understanding.
• Score: 67.12801626407135
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Efficiently understanding long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding and address a fundamental issue pertaining to all state-of-the-art (SOTA) long-context vision-language models (VLMs). Our contributions are twofold: First, we frame temporal search as a Long Video Haystack problem: finding a minimal set of relevant frames (e.g., one to five) from tens of thousands based on specific queries. Upon this formulation, we introduce LV-Haystack, the first dataset with 480 hours of videos, 15,092 human-annotated instances for both training and evaluation aiming to improve temporal search quality and efficiency. Results on LV-Haystack highlight a significant research gap in temporal search capabilities, with current SOTA search methods only achieving 2.1% temporal F1 score on the Longvideobench subset. Next, inspired by visual search in images, we propose a lightweight temporal search framework, T* that reframes costly temporal search as spatial search. T* leverages powerful visual localization techniques commonly used in images and introduces an adaptive zooming-in mechanism that operates across both temporal and spatial dimensions. Extensive experiments show that integrating T* with existing methods significantly improves SOTA long-form video understanding. Under an inference budget of 32 frames, T* improves GPT-4o's performance from 50.5% to 53.1% and LLaVA-OneVision-OV-72B's performance from 56.5% to 62.4% on the Longvideobench XL subset. Our code, benchmark, and models are provided in the Supplementary material.

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