Learning Resistant Binary Descriptors Against Noise for Efficient Image Retrieval

Hashing aims to learn a binary-output function that maps an image to a binary vector, which has received increasing attention with its potential in large-scale visual similarity search. Recently, supervised hashing methods have shown remarkable performance, but they assume that all examples are properly labeled. While in reality, it is unsurprising that we may encounter a range of label noise, which may significantly degrade retrieval performance. In response, we propose a noise-resistant Hashing Contrastive learning with hybrid selection (STAR). Specifically, STAR first develops noise-resistant hashing contrastive learning to preserve the similarity structure against label noise. In addition, we propose a hybrid sample selection strategy from the view of both Hamming distance and output uncertainty, which identifies reliable clean examples. Finally, to get rid of potential memorizing of noisy data, we incorporate both clean samples and noisy samples into selective centroid learning, which minimizes distances between clean samples and their centroids while pushing noisy samples away from negative centroids. Extensive experiments validate the efficacy of STAR.