MULTI-HIERARCHY PROXY STRUCTURE FOR DEEP METRIC LEARNING

Mainstream methods for deep metric learning can be divided into pair-based and proxy-based methods. In recent years, proxy-based methods have attracted wide attention for their low training complexity and fast network convergence. Most proxy-based studies assign only one proxy per class to capture the features of the class, this leads to ignoring the hidden hierarchy and regular aggregation of features within the class. However, these details are meaningful for capturing features of the class. Therefore, we propose a multi-hierarchy proxy (MHP) structure to extract the hierarchical details and regular features hidden in the embedding space. At the same time, we design a layerwise merging similarity operator to reasonably measure the similarity between samples and classes. Our MHP method maintains the low time complexity of the proxy-based method and can be easily integrated into existing proxy-based losses. The effectiveness of our method is evaluated by extensive experiments on three public datasets and compared with state-of-the-art methods. The results show that the proposed MHP method can significantly improve the performance of proxy-based methods, reaching 69.8% on CUB-200-2011 and 87.4% on Cars-196 dataset at Recall@1.