Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection

• URL: http://arxiv.org/abs/2501.10692v1
• Date: Sat, 18 Jan 2025 08:09:44 GMT
• Title: Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection
• Authors: Yifang Xu, Yunzhuo Sun, Benxiang Zhai, Zien Xie, Youyao Jia, Sidan Du,
• Abstract summary: Given a video and a linguistic query, video moment retrieval and highlight detection (MR&HD) aim to locate all the relevant spans while simultaneously predicting saliency scores.<n>Most existing methods utilize RGB images as input, overlooking the inherent multi-modal visual signals like optical flow and depth map.<n>We propose a Multi-modal Fusion and Query Refinement Network (MRNet) to learn complementary information from multi-modal cues.
• Score: 1.5236380958983644
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given a video and a linguistic query, video moment retrieval and highlight detection (MR&HD) aim to locate all the relevant spans while simultaneously predicting saliency scores. Most existing methods utilize RGB images as input, overlooking the inherent multi-modal visual signals like optical flow and depth. In this paper, we propose a Multi-modal Fusion and Query Refinement Network (MRNet) to learn complementary information from multi-modal cues. Specifically, we design a multi-modal fusion module to dynamically combine RGB, optical flow, and depth map. Furthermore, to simulate human understanding of sentences, we introduce a query refinement module that merges text at different granularities, containing word-, phrase-, and sentence-wise levels. Comprehensive experiments on QVHighlights and Charades datasets indicate that MRNet outperforms current state-of-the-art methods, achieving notable improvements in MR-mAP@Avg (+3.41) and HD-HIT@1 (+3.46) on QVHighlights.

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