Weighted Contrastive Hashing. (arXiv:2209.14099v1 [cs.CV])

The development of unsupervised hashing is advanced by the recent popular
contrastive learning paradigm. However, previous contrastive learning-based
works have been hampered by (1) insufficient data similarity mining based on
global-only image representations, and (2) the hash code semantic loss caused
by the data augmentation. In this paper, we propose a novel method, namely
Weighted Contrative Hashing (WCH), to take a step towards solving these two
problems. We introduce a novel mutual attention module to alleviate the problem
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