FineGrained Image Classification Combined with Label Description Finegrained image classification faces huge challenges because finegrained images are similar overall, and the distinguishable regions are difficult to find. Generally, in this task, label descriptions contain valuable semantic information that is accurately compatible with discriminative features of images (i.e., the description of the Rusty Black Bird corresponding to the morphological characteristics of its image). Bringing these descriptions into consideration is benefit to discern these similar images. Previous works, however, usually ignore label descriptions and just mine informative features from images, thus the performance may be limited. In this paper, we try to take both label descriptions and images into consideration, and we formalize the classification task into a matching task to address this issue. Specifically, Our model is based on a combination of Convolutional Neural Networks (CNN) over images and Graph Convolutional Networks(GCN) over label descriptions. We map the resulting image representations and text representations to the same dimension for matching and achieve the purpose of classification through the matching operation. Experimental results demonstrate that our approach can achieve the best performance compared with the stateoftheart methods on the datasets of Stanford dogs and CUB2002011.

Are These Birds Similar: Learning Branched Networks for Finegrained Representations Finegrained image classification is a challenging task due to the presence of hierarchical coarsetofinegrained distribution in the dataset. Generally, parts are used to discriminate various objects in finegrained datasets, however, not all parts are beneficial and indispensable. In recent years, natural language descriptions are used to obtain information on discriminative parts of the object. This paper leverages on natural language description and proposes a strategy for learning the joint representation of natural language description and images using a twobranch network with multiple layers to improve the finegrained classification task. Extensive experiments show that our approach gains significant improvements in accuracy for the finegrained image classification task. Furthermore, our method achieves new stateoftheart results on the CUB2002011 dataset.