Dual Attention Triplet Hashing Network for Image Retrieval - Jiang, Zhukai; Lian, Zhichao; Wang, Jinping (Frontiers in Neurorobotics, 2021)
In recent years, learning-based hashing techniques have proven to be efficient for large-scale image retrieval. However, since most of hash codes learned by deep hashing methods contain repetitive and correlated information, there are some limitations. In this paper, we propose a Dual Attention Triplet Hashing Network (DATH). DATH is implemented with two-stream ConvNet architecture. Specifically, the first neural network focuses on the spatial semantic relevance, and the second neural network focuses on the channel semantic correlation. These two neural networks are incorporated to create an end-to-end trainable framework. At the same time, in order to make better use of label information, DATH combines triplet likelihood loss and classification loss to optimize the network. Experimental results show that DATH has achieved the state-of-the-art performance on benchmark datasets.