Multi-scale context attention network for image retrieval

Recent attempts on the Convolutional Neural Network (CNN) based image retrieval usually adopt the output of a specific convolutional or fully connected layer as feature representation. Though superior representation capability has yielded better retrieval performance, the scale variation and clutter distracting remain to be two challenging problems in CNN based image retrieval. In this work, we propose a Multi-Scale Context Attention Network (MSCAN) to generate global descriptors, which is able to selectively focus on the informative regions with the assistance of multi-scale context information. We model the multi-scale context information by an improved Long Short-Term Memory (LSTM) network across different layers. As such, the proposed global descriptor is equipped with the scale aware attention capability. Experimental results show that our proposed method can effectively capture the informative regions in images and retain reliable attention responses when encountering scale variation and clutter distracting. Moreover, we compare the performance of the proposed scheme with the state-of-the-art global descriptors, and extensive results verify that the proposed MSCAN can achieve superior performance on several image retrieval benchmarks.