针对现有哈希算法所存在的特征提取能力有限、量化约束机制低效等问题，提出一个深度多尺度注意力哈希网络进行大规模图像检索。整个网络由主分支和对象分支两个子网络组成。其中，在主分支网络中加入多尺度注意力定位和显著性区域提取两个模块，以有效定位和提取图像中的显著性区域，并将执行结果送入对象分支网络学习更为丰富的细节特征。同时，将两个子网络学习到的多粒度特征进行融合并执行二进制哈希编码。此外，引入三元组量化约束以减少量化误差，同时保持成对样本的相似度关系。为验证算法的有效性，在两个基准数据集上进行了广泛实验。实验结果表明，所提方法优于大部分现有的哈希检索方法。

Aiming at the limited feature extraction capability and inefficient quantization constraint mechanism of existing hashing algorithms, a deep multi-scale attention hashing network was proposed for large-scale image retrieval. The whole network was composed of a main branch and a object branch. In the main branch, two modules of multi-scale attention localization and saliency region extraction were added to effectively localize and extract saliency regions of images, and the results were fed into the object branch to learn more detailed features. Subsequently, the multi-granularity features learned by two branches were fused to perform binary hash coding. In addition, a triplet quantization constraint was introduced to reduce quantization error while maintaining the similarity relationship between sample pairs. In order to verify the effectiveness of the proposed method, extensive experiments were carried out on two benchmark datasets. Experimental results showed that our method outperforms most existing hashing retrieval approaches.