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Self-Supervised Adversarial Hashing Networks for Cross-Modal Retrieval
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
doi:10.1109/cvpr.2018.00446
dblp:conf/cvpr/LiDL0GT18
fatcat:btx7fqba2raejio3txomwed3zi