Attention-guided Contrastive Hashing for Long-tailed Image Retrieval

Abstract

Image hashing is to represent an image using a binary code for efficient storage and accurate retrieval. Recently, deep hashing methods have shown great improvements on ideally balanced datasets, however, long-tailed data is more common due to rare samples or data collection costs in the real world. Toward that end, this paper introduces a simple yet effective model named Attention-guided Contrastive Hashing Network (ACHNet) for long-tailed hashing. Specifically, a cross attention feature enhancement module is proposed to predict the importance of features for hashing, alleviating the loss of information originated from data dimension reduction. Moreover, unlike recently sota contrastive methods that focus on instance-level discrimination, we optimize an innovative category-centered contrastive hashing to obtain discriminative results, which is more suitable for long-tailed scenarios. Experiments on two popular benchmarks verify the superiority of the proposed method. Our code is available at: https://github.com/KUXN98/ACHNet.