Learning to Focus and Discriminate for Fine-Grained Classification
Zhicong Feng, Keren Fu, Qijun Zhao · 2019
Existing state-of-the-art fine-grained classification methods usually use separated networks for discriminative region localization and feature learning/classification, and are thus complicated to implement and optimize. In this paper, we aim to provide a compact solution by deepening the collaboration between the region localization, feature learning and classification modules during the learning process of fine-grained classification. We thus propose a method that can learn to simultaneously localize discriminative regions and extract discriminative features by exploring the localization ability of classification convolutional neural networks and joint optimization of different modules. Our method, while being built upon a single backbone network and trained with only softmax losses, achieves state-of-the-art performance on three benchmark fine-grained datasets, which proves that our method is simple but effective for fine-grained classification.