Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval

Chao Li, Cheng Deng, Ning Li, Wei Liu, Xinbo Gao, Dacheng Tao
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
Thanks to the success of deep learning, cross-modal retrieval has made significant progress recently. However, there still remains a crucial bottleneck: how to bridge the modality gap to further enhance the retrieval accuracy. In this paper, we propose a self-supervised adversarial hashing (SSAH) approach, which lies among the early attempts to incorporate adversarial learning into cross-modal hashing in a self-supervised fashion. The primary contribution of this work is that two adversarial
more » ... works are leveraged to maximize the semantic correlation and consistency of the representations between different modalities. In addition, we harness a self-supervised semantic network to discover high-level semantic information in the form of multi-label annotations. Such information guides the feature learning process and preserves the modality relationships in both the common semantic space and the Hamming space. Extensive experiments carried out on three benchmark datasets validate that the proposed SSAH surpasses the state-of-the-art methods.
doi:10.1109/cvpr.2018.00446 dblp:conf/cvpr/LiDL0GT18 fatcat:btx7fqba2raejio3txomwed3zi