Contrastive Quantization with Code Memory for Unsupervised Image Retrieval

The high efficiency in computation and storage makes hashing (including binary hashing and quantization) a common strategy in large-scale retrieval systems. To alleviate the reliance on expensive annotations, unsupervised deep hashing becomes an important research problem. This paper provides a novel solution to unsupervised deep quantization, namely Contrastive Quantization with Code Memory (MeCoQ). Different from existing reconstruction-based strategies, we learn unsupervised binary descriptors by contrastive learning, which can better capture discriminative visual semantics. Besides, we uncover that codeword diversity regularization is critical to prevent contrastive learning-based quantization from model degeneration. Moreover, we introduce a novel quantization code memory module that boosts contrastive learning with lower feature drift than conventional feature memories. Extensive experiments on benchmark datasets show that MeCoQ outperforms state-of-the-art methods. Code and configurations are publicly released.

Authors

• Jinpeng Wang Tsinghua Shenzhen International Graduate School, Tsinghua University
• Ziyun Zeng Tsinghua Shenzhen International Graduate School, Tsinghua University Research Center of Artificial Intelligence, Peng Cheng Laboratory
• Bin Chen Harbin Institute of Technology, Shenzhen Research Center of Artificial Intelligence, Peng Cheng Laboratory
• Tao Dai Shenzhen University
• Shu-Tao Xia Tsinghua Shenzhen International Graduate School Pengcheng Laboratory

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