Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification

Learning subtle discriminative features plays a significant role in fine-grained image classification. Existing methods usually extract the distinguishable parts through the attention module for classification. Although these learned distinguishable parts contain valuable features that are beneficial for classification, part of irrelevant features are also preserved, which may confuse the model to make a correct classification, especially for the fine-grained tasks due to their similarities. How to keep the discriminative features while removing confusable features from the distinguishable parts is an interesting yet changeling task. In this paper, we introduce a novel classification approach, named Logical-based Feature Extraction Model (LAFE for short) to address this issue. The main advantage of LAFE lies in the fact that it can explicitly add the significance of discriminative features and subtract the confusable features. Specifically, LAFE utilizes the region attention modules and channel attention modules to extract discriminative features and confusable features respectively. Based on this, two novel loss functions are designed to automatically induce attention over these features for fine-grained image classification. Our approach demonstrates its robustness, efficiency, and state-of-the-art performance on three benchmark datasets.