Label hierarchy constraint network for fine-grained classification

Abstract

Recognize fine grained categories is challenging task, which has attracted more and more attention in recent years. Different from traditional image recognition, fine-grained image recognition is to recognize different sub-classes under the same general category. Due to posture, illumination and other reasons, fine grained recognition has large intra-class diversities and subtle inter-class similarities, Most of the works focused on how to localize discriminative regions and fine-grained feature learning. But they negative the structure of the fine grained labels. In this paper, we propose label hierarchy constraint network (LHC) for fine-grained classification. The network include two branchs, coarse-level branch and fine level branch. In the middle layers of the neural networks, we use the coarse branch to predict the coarse labels, which can be regarded as the guidance of fine-grained labels. In the upper layers, we predict fine-grained labels. Then we map the result of coarse-grained branch prediction probability distribution to the fine-grained branch. Experiments show the effectiveness of our method.