# Channel Interaction Networks for Fine-Grained Image Categorization

arXiv: https://arxiv.org/abs/2003.05235 · HTML: https://mlsanity.com/paper/2003.05235/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 30 of 47)

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- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Dual Cross-Attention Learning for Fine-Grained Visual Categorization and Object Re-Identification](https://mlsanity.com/paper/2205.02151/) (2022)
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- [Dynamic MLP for Fine-Grained Image Classification by Leveraging Geographical and Temporal Information](https://mlsanity.com/paper/2203.03253/) (2022)
- [Adaptive Fine-Grained Predicates Learning for Scene Graph Generation](https://mlsanity.com/paper/2207.04602/) (2022)
- [Delving into Multimodal Prompting for Fine-grained Visual Classification](https://mlsanity.com/paper/2309.08912/) (2023)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [Transformer with Peak Suppression and Knowledge Guidance for Fine-grained Image Recognition](https://mlsanity.com/paper/2107.06538/) (2021)
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- [Part-guided Relational Transformers for Fine-grained Visual Recognition](https://mlsanity.com/paper/2212.13685/) (2022)
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- [Fine-Grained Self-Supervised Learning with Jigsaw Puzzles for Medical Image Classification](https://mlsanity.com/paper/2308.05770/) (2023)
- [ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning](https://mlsanity.com/paper/2503.19331/) (2025)
- [Progressive Co-Attention Network for Fine-grained Visual Classification](https://mlsanity.com/paper/2101.08527/) (2021)
- [Convolutional Fine-Grained Classification with Self-Supervised Target Relation Regularization](https://mlsanity.com/paper/2208.01997/) (2022)
- [Channel DropBlock: An Improved Regularization Method for Fine-Grained Visual Classification](https://mlsanity.com/paper/2106.03432/) (2021)
- [A Discriminative Channel Diversification Network for Image Classification](https://mlsanity.com/paper/2112.05861/) (2021)
- [Fine-Grained Visual Classification via Simultaneously Learning of Multi-regional Multi-grained Features](https://mlsanity.com/paper/2102.00367/) (2021)
- [A Feature Consistency Driven Attention Erasing Network for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2110.04479/) (2021)
- [Salient Mask-Guided Vision Transformer for Fine-Grained Classification](https://mlsanity.com/paper/2305.07102/) (2023)
- [FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features](https://mlsanity.com/paper/2408.16881/) (2024)
- [QCS: Feature Refining from Quadruplet Cross Similarity for Facial Expression Recognition](https://mlsanity.com/paper/2411.01988/) (2024)
- [Feature Boosting, Suppression, and Diversification for Fine-Grained Visual Classification](https://mlsanity.com/paper/2103.02782/) (2021)
- [Bridge the Gap between Supervised and Unsupervised Learning for Fine-Grained Classification](https://mlsanity.com/paper/2203.00441/) (2022)
- [Parsing Objects at a Finer Granularity: A Survey](https://mlsanity.com/paper/2212.13693/) (2022)
- [Kernel function impact on convolutional neural networks](https://mlsanity.com/paper/2302.10266/) (2023)

## Abstract (arXiv)

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).