Weighted Focus-Attention Deep Network for Fine-grained Image Classification

Cong Zou, Rui Wang, Xiaochun Cao, Feixiao Lv

Abstract: Fine-Grained Visual Classification (FGVC) is a challenging task, due to the small variation of visual representations from different categories. An effective solution is utilizing the bounding boxes centering the object parts to extract the discriminative representations. However, regular rectangles contains the background when the shape of the part is irregular, which may interfere with the classification. In this paper, we propose a weighted focus-attention deep network (FA-Net) to address the problem of background interference in fine-grained classification. In our FA-Net, a focus-attention module is proposed to identify the foreground region from the class activation map and remove the background. Two branches are employed to obtain the primary and secondary attention regions with focus-attention module, and a weighted layer is utilized to integrate the attention regions. Experiment results on three challenging fine-grained classification datasets (e.g., CUB-200-2011, Stanford Dogs and FGVC Aircraft) show that our FA-Net obtains state-of-the-art results and outperforms the other fine-grained algorithms.