Improved fine-grained object retrieval with Hard Global Softmin Loss objective

Abstract

Image retrieval is a general task in computer vision, which aims at returning similar images of the query. Nowadays, extensive research has been drawn to fine-grained object retrieval, which is one of the difficult tasks of image retrieval. Compared to general image retrieval, the data of fine-grained objects show a great diversity in the same class, while a small diversity in different classes. Therefore, the key to fine-grained object retrieval resides in training models to obtain discriminative features. A kind of methods are based on local structure loss functions, e.g. pairwise and triplet loss, to generate distinguishable features for fine-grained object retrieval. However, these methods are time-consuming at the training stage and of low accuracy. To solve these problems, some methods based on global structure loss functions are proposed. Convolutional neural networks are optimized with the global centers and then generate distinguishable features. In this paper, based on the global structure loss functions and hard mining strategy, we propose the Hard Global Softmin Loss to improve the performance of fine-grained object retrieval. Furthermore, a learnable parameter is introduced into the proposed loss, which is dynamically adjusted by the network throughout the training. Lots of experiments show that the proposed loss function is effective and helpful for promoting the retrieval performance. Specifically, significant improvements are obtained over the state-of-the-art on four popular fine-grained datasets in our experiments11Our code is publicly available at https://github.com/RiyaoDong/HGSL..