  Fine-grained image categorization is challenging due to the subtle
inter-class differences.We posit that exploiting the rich relationships between
channels can help capture such differences since different channels correspond
to different semantics. In this paper, we propose a channel interaction network
(CIN), which models the channel-wise interplay both within an image and across
images. For a single image, a self-channel interaction (SCI) module is proposed
to explore channel-wise correlation within the image. This allows the model to
learn the complementary features from the correlated channels, yielding
stronger fine-grained features. Furthermore, given an image pair, we introduce
a contrastive channel interaction (CCI) module to model the cross-sample
channel interaction with a metric learning framework, allowing the CIN to
distinguish the subtle visual differences between images. Our model can be
trained efficiently in an end-to-end fashion without the need of multi-stage
training and testing. Finally, comprehensive experiments are conducted on three
publicly available benchmarks, where the proposed method consistently
outperforms the state-of-theart approaches, such as DFL-CNN (Wang, Morariu, and
Davis 2018) and NTS (Yang et al. 2018).
