A Multi-View Double Alignment Hashing Network with Weighted Contrastive Learning

Tianlong Zhang, Zhe Xue, Yuchen Dong, Junping Du, Meiyu Liang

Abstract: Multi-view retrieval faces significant pressure due to the rapidly increasing multi-view information on the internet. The multi-view hashing method turns continuous features into compact information of fixed length and considerably improves retrieval efficiency. However, existing multi-view hashing methods neglect the bias produced during multi-view alignment and multi-label guidance processes. To address these issues, we introduce a novel multi-view hash method that learns compact hash codes. It first employs a multi-view double alignment module to align features from different views. Then, it utilizes a self-adjusted cross-attention fusion module to fuse these features. Finally, we propose a weighted contrastive learning module to learn more discriminative representations, smoothing the differences among all samples. Extensive experiments show that our method yields compact hash codes and outperforms state-of-the-art methods.