Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916853785296896 |
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| author | Zhang, Deyu Long, Tingting Zhang, Jinrui Chen, Ligeng Ren, Ju Zhang, Yaoxue |
| author_facet | Zhang, Deyu Long, Tingting Zhang, Jinrui Chen, Ligeng Ren, Ju Zhang, Yaoxue |
| contents | Enabling efficient text-video retrieval on edge-end devices is critical for real-world applications. Yet, existing methods face a critical challenge in balancing accuracy and computational efficiency: uniform frame sampling methods ensure content coverage but incur prohibitive computational costs, while salient-frame sampling methods reduce overhead but suffer from query-agnostic frame selection that biases retrieval results. To address this, we propose ProCLIP, a user-centric framework that achieves state-of-the-art accuracy with significantly improved efficiency. We design a prompt-aware frame sampling strategy that dynamically guides lightweight feature extractors using textual prompts to select semantically relevant frames, overcoming the limitations of existing salient-frame sampling methods which rely on static, query-agnostic selection criteria. Moreover, we adopt a two-stage candidate pruning strategy that combines rapid coarse filtering via a lightweight module with CLIP-powered fine-grained re-ranking, enhancing retrieval efficiency while preserving accuracy. Experiments across benchmarks show ProCLIP achieves 75.3% latency reduction versus baselines while maintaining competitive accuracy, i.e., R@1=49.0 in MSR-VTT dataset. Code is available at https://github.com/tiffylong/ProCLIP. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval Zhang, Deyu Long, Tingting Zhang, Jinrui Chen, Ligeng Ren, Ju Zhang, Yaoxue Multimedia Computer Vision and Pattern Recognition Enabling efficient text-video retrieval on edge-end devices is critical for real-world applications. Yet, existing methods face a critical challenge in balancing accuracy and computational efficiency: uniform frame sampling methods ensure content coverage but incur prohibitive computational costs, while salient-frame sampling methods reduce overhead but suffer from query-agnostic frame selection that biases retrieval results. To address this, we propose ProCLIP, a user-centric framework that achieves state-of-the-art accuracy with significantly improved efficiency. We design a prompt-aware frame sampling strategy that dynamically guides lightweight feature extractors using textual prompts to select semantically relevant frames, overcoming the limitations of existing salient-frame sampling methods which rely on static, query-agnostic selection criteria. Moreover, we adopt a two-stage candidate pruning strategy that combines rapid coarse filtering via a lightweight module with CLIP-powered fine-grained re-ranking, enhancing retrieval efficiency while preserving accuracy. Experiments across benchmarks show ProCLIP achieves 75.3% latency reduction versus baselines while maintaining competitive accuracy, i.e., R@1=49.0 in MSR-VTT dataset. Code is available at https://github.com/tiffylong/ProCLIP. |
| title | Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval |
| topic | Multimedia Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.15491 |