  Fine-grained visual classification aims to recognize images belonging to
multiple sub-categories within a same category. It is a challenging task due to
the inherently subtle variations among highly-confused categories. Most
existing methods only take individual image as input, which may limit the
ability of models to recognize contrastive clues from different images. In this
paper, we propose an effective method called progressive co-attention network
(PCA-Net) to tackle this problem. Specifically, we calculate the channel-wise
similarity by interacting the feature channels within same-category images to
capture the common discriminative features. Considering that complementary
imformation is also crucial for recognition, we erase the prominent areas
enhanced by the channel interaction to force the network to focus on other
discriminative regions. The proposed model can be trained in an end-to-end
manner, and only requires image-level label supervision. It has achieved
competitive results on three fine-grained visual classification benchmark
datasets: CUB-200-2011, Stanford Cars, and FGVC Aircraft.
