Channel Interaction Networks for Fine-Grained Image Categorization


  • Yu Gao Malong Technologies, Shenzhen, China
  • Xintong Han Malong Technologies, Shenzhen, China
  • Xun Wang Malong Technologies, Shenzhen, China
  • Weilin Huang Malong Technologies, Shenzhen, China
  • Matthew Scott Malong Technologies, Shenzhen, China



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-the-art approaches, such as DFL-CNN(Wang, Morariu, and Davis 2018) and NTS(Yang et al. 2018).




How to Cite

Gao, Y., Han, X., Wang, X., Huang, W., & Scott, M. (2020). Channel Interaction Networks for Fine-Grained Image Categorization. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 10818-10825.



AAAI Technical Track: Vision