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arXiv · 2507.15491

Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval

Deyu Zhang·Tingting Long·Jinrui Zhang·Ligeng Chen·Ju Ren·Yaoxue Zhang
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Abstract

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.

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Deyu Zhang, Tingting Long, Jinrui Zhang, Ligeng Chen, Ju Ren, Yaoxue Zhang. 2025-07-21. Prompt-aware of Frame Sampling for Efficient Text-Video Retrieval. https://arxiv.org/abs/2507.15491

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