# Learning Semantically Enhanced Feature for Fine-Grained Image Classification

arXiv: https://arxiv.org/abs/2006.13457 · HTML: https://mlsanity.com/paper/2006.13457/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

## Built on by (most cited first, 12 of 12)

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- [R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction](https://mlsanity.com/paper/2204.10095/) (2022)
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- [ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator](https://mlsanity.com/paper/2203.12816/) (2022)
- [Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition](https://mlsanity.com/paper/2401.14336/) (2024)
- [Semantic Feature Integration network for Fine-grained Visual Classification](https://mlsanity.com/paper/2302.10275/) (2023)
- [EnGraf-Net: Multiple Granularity Branch Network with Fine-Coarse Graft Grained for Classification Task](https://mlsanity.com/paper/2509.21061/) (2025)

## Abstract (arXiv)

We aim to provide a computationally cheap yet effective approach for fine-grained image classification (FGIC) in this letter. Unlike previous methods that rely on complex part localization modules, our approach learns fine-grained features by enhancing the semantics of sub-features of a global feature. Specifically, we first achieve the sub-feature semantic by arranging feature channels of a CNN into different groups through channel permutation. Meanwhile, to enhance the discriminability of sub-features, the groups are guided to be activated on object parts with strong discriminability by a weighted combination regularization. Our approach is parameter parsimonious and can be easily integrated into the backbone model as a plug-and-play module for end-to-end training with only image-level supervision. Experiments verified the effectiveness of our approach and validated its comparable performance to the state-of-the-art methods. Code is available at https://github.com/cswluo/SEF