Deep Product Quantization Module For Efficient Image Retrieval
2020 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING(2020)
摘要
Product Quantization (PQ) is one of the most popular Approximate Nearest Neighbor (ANN) methods for large-scale image retrieval, bringing better performance than hashing based methods. In recent years, several works extend the hard quantization to soft quantization with specially designed deep neural architectures. We propose a simple but effective deep Product Quantization Module (PQM) to jointly learn discriminative codebook and precise hard assignment in an end-to-end manner. In this work, we use the straight-through estimator to make it feasible to directly optimize the discrete binary representations in deep neural networks with stochastic gradient descent. Different from previous deep vector quantization methods, PQM is a plug-and-play module which can be adaptive to various base networks in the scenarios of image search or compression. Besides, we propose a reconstruction loss to minimize the domain gap between the original embedding features and codebook. Experimental results show that PQM outperforms state-of-the-art deep supervised hashing and quantization methods on several image retrieval benchmarks.
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关键词
Product Quantization, Hashing, Deep Learning
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