自适应混合注意力深度跨模态哈希

针对现有哈希方法在特征学习过程中无法区分各区域特征信息的重要程度和不能充分利用标签信息来深度挖掘模态间相关性的问题，提出了自适应混合注意力深度跨模态哈希检索（AHAH）模型。首先，通过自主学习得到的权重将通道注意力和空间注意力有机结合来强化对特征图中相关目标区域的关注度，同时弱化对不相关目标区域的关注度；其次，通过对模态标签进行统计分析，并使用所提出的相似度计算方法将相似度量化为0～1的数字以更精细地表示模态间的相似性。在4个常用的数据集MIRFLICKR-25K、NUS-WIDE、MSCOCO和IAPR TC-12上，当哈希码长度为16 bit时，与最先进的方法多标签语义保留哈希（MLSPH）相比，所提方法的检索平均准确率均值（mAP）分别提高了2.25%、1.75%、6.8%和2.15%。此外，消融实验和效率分析也证明了所提方法的有效性。

Adaptive hybrid attention hashing for deep cross-modal retrieval

In feature learning process， the existing hashing methods cannot distinguish the importance of the feature information of each region， and cannot utilize the label information to explore the correlation between modalities. Therefore， an Adaptive Hybrid Attention Hashing for deep cross-modal retrieval （AHAH） model was proposed. Firstly， channel attention and spatial attention were combined by the weights obtained by autonomous learning to strengthen the attention to the relevant target area and weaken the attention to the irrelevant target area. Secondly， the similarity between modalities was expressed more finely through the statistical analysis of modality labels and quantification of similarity degrees to numbers between 0 and 1 by using the proposed similarity measurement method. Compared with the most advanced method Multi-Label Semantics Preserving Hashing （MLSPH） on four commonly used datasets MIRFLICKR-25K， NUS-WIDE， MSCOCO， and IAPR TC-12， when the hash code length is 16 bit， the proposed method has the retrieval mean Average Precision （mAP） increased by 2.25%， 1.75%， 6.8%， and 2.15%， respectively. In addition， ablation experiments and efficiency analysis also prove the effectiveness of the proposed method.