Attention Bilinear Pooling for Fine-Grained Classification

Abstract

Fine-grained image classification is a challenging problem because of its large intra-class differences and low inter-class variance. Bilinear pooling based models have been shown to be effective at fine-grained classification, while most previous approaches neglect the fact that distinctive features or modeling distinguishing regions usually have an important role in solving the fine-grained problem. In this paper, we propose a novel convolutional neural network framework, i.e., attention bilinear pooling, for fine-grained classification with attention. This framework can learn the distinctive feature information from the channel or spatial attention. Specifically, the channel and spatial attention allows the network to better focus on where the key targets are in the image. This paper embeds spatial attention and channel attention in the underlying network architecture to better represent image features. To further explore the differences between channels and spatial attention, we propose channel attention bilinear pooling (CAB), spatial attention bilinear pooling (SAB), channel spatial attention bilinear pooling (CSAB), and spatial channel attention bilinear pooling (SCAB) as four alternative frames. A variety of experiments on several datasets show that our proposed method has a very impressive performance compared to other methods based on bilinear pooling.