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[2503.13444] VideoMind: A Chain-of-LoRA Agent for Temporal-Grounded Video Reasoning
Summary
VideoMind introduces a novel approach for temporal-grounded video reasoning using a Chain-of-LoRA agent, enhancing multi-modal reasoning capabilities in videos.
Why It Matters
As video content becomes increasingly prevalent, effective reasoning over temporal dimensions is crucial. This research addresses the limitations of current video-language models, providing a framework that improves understanding and interaction with video data, which is vital for advancements in AI applications such as surveillance, entertainment, and education.
Key Takeaways
- VideoMind proposes a role-based workflow for video reasoning, enhancing multi-modal understanding.
- The Chain-of-LoRA mechanism allows efficient role switching, balancing performance and flexibility.
- Extensive testing on 15 benchmarks demonstrates significant advancements in video reasoning tasks.
Computer Science > Computer Vision and Pattern Recognition arXiv:2503.13444 (cs) [Submitted on 17 Mar 2025 (v1), last revised 21 Feb 2026 (this version, v3)] Title:VideoMind: A Chain-of-LoRA Agent for Temporal-Grounded Video Reasoning Authors:Ye Liu, Kevin Qinghong Lin, Chang Wen Chen, Mike Zheng Shou View a PDF of the paper titled VideoMind: A Chain-of-LoRA Agent for Temporal-Grounded Video Reasoning, by Ye Liu and 3 other authors View PDF HTML (experimental) Abstract:Videos, with their unique temporal dimension, demand precise grounded understanding, where answers are directly linked to visual, interpretable evidence. Despite significant breakthroughs in text-based reasoning with large language models, multi-modal reasoning - especially for videos - remains limited. In this work, we fill this gap by introducing VideoMind, a novel video-language agent for temporal-grounded video reasoning. Our method involves two key innovations: (1) We identify four essential capabilities for grounded video reasoning and propose a role-based agentic workflow, comprising a planner to coordinate roles, a grounder for temporal event localization, a verifier to assess event candidates, and an answerer for question answering. (2) To efficiently integrate these roles during inference, we propose a novel Chain-of-LoRA mechanism, where a unified base model with multiple LoRA adapters is leveraged to enable seamless role switching, balancing efficiency and flexibility. Extensive experiments on 15...
