Self-supervised Adversarial Hashing for Large-scale Image Retrieval

Yuan Cao, Xue Xu, Junwei Liu, Xiangru Chen

Abstract: Hashing has been widely used in large-scale image retrieval due to its high storage and search efficiency. Unsupervised learning saves a significant amount of labor costs compared to supervised learning. Existing unsupervised methods mostly convert unsupervised problems into supervised problems by reconstructing semantic information. In this paper, we propose a novel Self-supervised Adversarial Hashing (SAH) method which utilizes unsupervised semantic reconstruction methods along with self-supervised generative adversarial and contrastive learning methods to improve the model’s accuracy and robustness. In addition, we propose a novel method for semantic relationship reconstruction, taking into account the similarity between different categories. Based on this, we have designed a multi-joint loss to further achieve reasonable intra-class aggregation and inter-class differentiation. The experimental results show that the proposed method outperforms state-of-the-art hashing methods for large-scale image retrieval.