Dual Dynamic Proxy Hashing Network for Long-tailed Image Retrieval

Deep hashing has been extensively explored for image retrieval due to fast computation and efficient storage. Since conventional deep hashing methods are not suitable for the common scenario in real life that data exhibits a long-tailed distribution, several long-tailed hashing methods have been proposed recently. However, existing long-tail hashing methods seek to utilize fixed class centroids and cannot fully develop the discriminative ability of hash codes for tail-class samples. Specifically, fixed class centroids cannot characterize authentic semantics of tail classes or provide effective semantic information for hash codes learning under the long-tailed setting. To this end, we propose a novel Dual Dynamic Proxy Hashing Network (DDPHN) with two sets of learnable dynamic proxies, i.e. hash proxies and feature proxies, to improve the discrimination of hash codes for tail-class samples. Compared with fixed class centroids, learnable proxies can be optimized constantly via the proxy learning loss and depict accurate class semantics despite the scarcity of tail-class samples. Apart from low-dimensional binary hash proxies, we introduce high-dimensional continuous feature proxies that can describe semantic relationships more precisely, contributing to hash codes learning as well. To further leverage semantic information carried by proxies, we build a hypergraph by exploring neighborhood relationships in the feature space and then introduce a hypergraph neural network to transfer knowledge from proxies to samples in the Hamming space. Extensive experiments show the superiority of our learnable dynamic proxies and demonstrate that our method outperforms numerous deep hashing models and recent state-of-the-art long-tailed hashing methods.