MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding

• URL: http://arxiv.org/abs/2506.08512v1
• Date: Tue, 10 Jun 2025 07:20:12 GMT
• Title: MLVTG: Mamba-Based Feature Alignment and LLM-Driven Purification for Multi-Modal Video Temporal Grounding
• Authors: Zhiyi Zhu, Xiaoyu Wu, Zihao Liu, Linlin Yang,
• Abstract summary: Video Temporal Grounding aims to localize video clips corresponding to natural language queries.<n>Existing Transformer-based methods often suffer from redundant attention and suboptimal multi-modal alignment.<n>We propose MLVTG, a novel framework that integrates two key modules: MambaAligner and LLMRefiner.
• Score: 13.025856914576673
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video Temporal Grounding (VTG), which aims to localize video clips corresponding to natural language queries, is a fundamental yet challenging task in video understanding. Existing Transformer-based methods often suffer from redundant attention and suboptimal multi-modal alignment. To address these limitations, we propose MLVTG, a novel framework that integrates two key modules: MambaAligner and LLMRefiner. MambaAligner uses stacked Vision Mamba blocks as a backbone instead of Transformers to model temporal dependencies and extract robust video representations for multi-modal alignment. LLMRefiner leverages the specific frozen layer of a pre-trained Large Language Model (LLM) to implicitly transfer semantic priors, enhancing multi-modal alignment without fine-tuning. This dual alignment strategy, temporal modeling via structured state-space dynamics and semantic purification via textual priors, enables more precise localization. Extensive experiments on QVHighlights, Charades-STA, and TVSum demonstrate that MLVTG achieves state-of-the-art performance and significantly outperforms existing baselines.

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