Channel Interaction Networks for Fine-Grained Image Categorization

Abstract

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).

arXiv commentAAAI 2020

References in corpus (14)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Understanding Neural Networks Through Deep Visualization
• Fine-Grained Visual Classification of Aircraft
• Non-local Neural Networks
• Compact Generalized Non-local Network
• Beyond triplet loss: a deep quadruplet network for person re-identification
• Learning Fine-grained Image Similarity with Deep Ranking
• Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-grained Image Recognition
• Dynamic Computational Time for Visual Attention
• Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition
• Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition
• Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning
• Fine-Grained Representation Learning and Recognition by Exploiting Hierarchical Semantic Embedding
• Learning to Navigate for Fine-grained Classification