Chen, S.; Wu, S.; Wang, L.; Yu, Z. Self-Attention and Adversary Learning Deep Hashing Network for Cross-Modal Retrieval. Computers & Electrical Engineering 2021, 93, 107262, doi:10.1016/j.compeleceng.2021.107262.
Chen, S.; Wu, S.; Wang, L.; Yu, Z. Self-Attention and Adversary Learning Deep Hashing Network for Cross-Modal Retrieval. Computers & Electrical Engineering 2021, 93, 107262, doi:10.1016/j.compeleceng.2021.107262.
Chen, S.; Wu, S.; Wang, L.; Yu, Z. Self-Attention and Adversary Learning Deep Hashing Network for Cross-Modal Retrieval. Computers & Electrical Engineering 2021, 93, 107262, doi:10.1016/j.compeleceng.2021.107262.
Chen, S.; Wu, S.; Wang, L.; Yu, Z. Self-Attention and Adversary Learning Deep Hashing Network for Cross-Modal Retrieval. Computers & Electrical Engineering 2021, 93, 107262, doi:10.1016/j.compeleceng.2021.107262.
Abstract
Recently deep cross-modal hashing networks have received increasing interests due to its superior query efficiency and low storage cost. However, most of existing methods concentrate less on hash representations learning part, which means the semantic information of data cannot be fully used. Furthermore, they may neglect the high-ranking relevance and consistency of hash codes. To solve these problems, we propose a Self-Attention and Adversary Guided Hashing Network (SAAGHN). Specifically, it employs self-attention mechanism in hash representations learning part to extract rich semantic relevance information. Meanwhile, in order to keep invariability of hash codes, adversarial learning is adopted in the hash codes learning part. In addition, to generate higher-ranking hash codes and avoid local minima early, a new batch semi-hard cosine triplet loss and a cosine quantization loss are proposed. Extensive experiments on two benchmark datasets have shown that SAAGHN outperforms other baselines and achieves the state-of-the-art performance.
Keywords
adversarial learning; deep cross-modal hashing; self-attention mechanism
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
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