Delving Deeper: Hierarchical Visual Perception for Robust Video-Text Retrieval
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917209703448576 |
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| author | Xie, Zequn Zhang, Boyun Lin, Yuxiao Jin, Tao |
| author_facet | Xie, Zequn Zhang, Boyun Lin, Yuxiao Jin, Tao |
| contents | Video-text retrieval (VTR) aims to locate relevant videos using natural language queries. Current methods, often based on pre-trained models like CLIP, are hindered by video's inherent redundancy and their reliance on coarse, final-layer features, limiting matching accuracy. To address this, we introduce the HVP-Net (Hierarchical Visual Perception Network), a framework that mines richer video semantics by extracting and refining features from multiple intermediate layers of a vision encoder. Our approach progressively distills salient visual concepts from raw patch-tokens at different semantic levels, mitigating redundancy while preserving crucial details for alignment. This results in a more robust video representation, leading to new state-of-the-art performance on challenging benchmarks including MSRVTT, DiDeMo, and ActivityNet. Our work validates the effectiveness of exploiting hierarchical features for advancing video-text retrieval. Our codes are available at https://github.com/boyun-zhang/HVP-Net. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_12768 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Delving Deeper: Hierarchical Visual Perception for Robust Video-Text Retrieval Xie, Zequn Zhang, Boyun Lin, Yuxiao Jin, Tao Computer Vision and Pattern Recognition Multimedia Video-text retrieval (VTR) aims to locate relevant videos using natural language queries. Current methods, often based on pre-trained models like CLIP, are hindered by video's inherent redundancy and their reliance on coarse, final-layer features, limiting matching accuracy. To address this, we introduce the HVP-Net (Hierarchical Visual Perception Network), a framework that mines richer video semantics by extracting and refining features from multiple intermediate layers of a vision encoder. Our approach progressively distills salient visual concepts from raw patch-tokens at different semantic levels, mitigating redundancy while preserving crucial details for alignment. This results in a more robust video representation, leading to new state-of-the-art performance on challenging benchmarks including MSRVTT, DiDeMo, and ActivityNet. Our work validates the effectiveness of exploiting hierarchical features for advancing video-text retrieval. Our codes are available at https://github.com/boyun-zhang/HVP-Net. |
| title | Delving Deeper: Hierarchical Visual Perception for Robust Video-Text Retrieval |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2601.12768 |