A Ranked Similarity Loss Function with pair Weighting for Deep Metric Learning

Abstract

Metric learning is a widely-used method for image retrieval. The object of metric learning is to limit the distance between similar samples and increase the distance between samples of different classes through learning. Many studies tend to pay more attention to keep the distance between positive and negative samples, but ignore the distance between different classes of negative samples. In fact, query samples should be separated from negative samples of different classes by different distances. To address these problems, we propose to build a ranked similarity loss function with pair weighting (dubbed RMS loss). The proposed RMS loss can keep a distance between samples of different classes by weighting the negative samples according to the sorting order. Meanwhile, it further widens the distance between positive and negative samples by different processing of similarity of positive pairs and negative pairs. The effectiveness of our method is evaluated by extensive experiments on four public datasets and compared with state-of-the-art methods. The results show the proposed method obtains new performance on four public datasets, e.g., reaching 67.4% on CUB200 at Recall@1.