See, Rank, and Filter: Important Word-Aware Clip Filtering via Scene Understanding for Moment Retrieval and Highlight Detection

• URL: http://arxiv.org/abs/2511.22906v1
• Date: Fri, 28 Nov 2025 06:22:50 GMT
• Title: See, Rank, and Filter: Important Word-Aware Clip Filtering via Scene Understanding for Moment Retrieval and Highlight Detection
• Abstract summary: Video moment retrieval (MR) and highlight detection (HD) with natural language queries aim to localize relevant moments and key highlights in a video clips.<n>Existing methods overlook the importance of individual words, treating the entire text query and video clips as a black-box.<n>We propose a novel approach that enables fine-grained clip filtering by identifying and prioritizing important words in the query.
• Score: 21.187420225759183
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video moment retrieval (MR) and highlight detection (HD) with natural language queries aim to localize relevant moments and key highlights in a video clips. However, existing methods overlook the importance of individual words, treating the entire text query and video clips as a black-box, which hinders contextual understanding. In this paper, we propose a novel approach that enables fine-grained clip filtering by identifying and prioritizing important words in the query. Our method integrates image-text scene understanding through Multimodal Large Language Models (MLLMs) and enhances the semantic understanding of video clips. We introduce a feature enhancement module (FEM) to capture important words from the query and a ranking-based filtering module (RFM) to iteratively refine video clips based on their relevance to these important words. Extensive experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods, achieving superior performance in both MR and HD tasks. Our code is available at: https://github.com/VisualAIKHU/SRF.

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