Contrastive Quantization with Code Memory for Unsupervised Image Retrieval
This addresses the problem of efficient large-scale image retrieval without expensive annotations, representing an incremental improvement over existing methods.
The paper tackles unsupervised deep quantization for image retrieval by introducing MeCoQ, which uses contrastive learning and a code memory module to learn binary descriptors, achieving state-of-the-art performance on benchmark datasets.
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 available at https://github.com/gimpong/AAAI22-MeCoQ.