自动选难帧提升视频哈希效率与精度
AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing
- 用对抗采样自动挑选信息量大的关键帧
- 在多个数据集上达到最优检索准确率
- 适合需要高效视频检索的工程应用
自监督视频哈希（SSVH）利用无标签视频将视频压缩为哈希码，以实现高效索引与检索。现有方法依赖随机帧采样学习视频特征，并对所有帧同等对待，忽略了帧间的信息密度差异与重建难度，导致哈希码性能不佳。为此，我们提出AutoSSVH框架，采用基于哈希的对抗性帧采样策略，自动识别并选择更难重建的高信息量帧，增强编码能力。同时引入哈希组件投票机制与点到集合（P2Set）哈希对比目标，捕捉哈希空间中复杂的跨视频语义关系，提升哈希码的区分度。大量实验表明，与现有最佳方法相比，AutoSSVH在检索效果与效率上均表现更优。代码已公开于 https://github.com/EliSpectre/CVPR25-AutoSSVH。
原文摘要 · Abstract (English)
Self-Supervised Video Hashing (SSVH) compresses videos into hash codes for efficient indexing and retrieval using unlabeled training videos. Existing approaches rely on random frame sampling to learn video features and treat all frames equally. This results in suboptimal hash codes, as it ignores frame-specific information density and reconstruction difficulty. To address this limitation, we propose a new framework, termed AutoSSVH, that employs adversarial frame sampling with hash-based contrastive learning. Our adversarial sampling strategy automatically identifies and selects challenging frames with richer information for reconstruction, enhancing encoding capability. Additionally, we introduce a hash component voting strategy and a point-to-set (P2Set) hash-based contrastive objective, which help capture complex inter-video semantic relationships in the Hamming space and improve the discriminability of learned hash codes. Extensive experiments demonstrate that AutoSSVH achieves superior retrieval efficacy and efficiency compared to state-of-the-art approaches. Code is available at https://github.com/EliSpectre/CVPR25-AutoSSVH.
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