# A Novel Plug-in Module for Fine-Grained Visual Classification

arXiv: https://arxiv.org/abs/2202.03822 · HTML: https://mlsanity.com/paper/2202.03822/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 53 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 30 of 41)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Fixing the train-test resolution discrepancy](https://mlsanity.com/paper/1906.06423/) (2019)
- [Weakly Supervised Deep Detection Networks](https://mlsanity.com/paper/1511.02853/) (2015)
- [Object Detectors Emerge in Deep Scene CNNs](https://mlsanity.com/paper/1412.6856/) (2014)
- [Adversarial Complementary Learning for Weakly Supervised Object Localization](https://mlsanity.com/paper/1804.06962/) (2018)
- [TransFG: A Transformer Architecture for Fine-grained Recognition](https://mlsanity.com/paper/2103.07976/) (2021)
- [Multiple Instance Detection Network with Online Instance Classifier Refinement](https://mlsanity.com/paper/1704.00138/) (2017)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [Learning to Navigate for Fine-grained Classification](https://mlsanity.com/paper/1809.00287/) (2018)
- [Learning Attentive Pairwise Interaction for Fine-Grained Classification](https://mlsanity.com/paper/2002.10191/) (2020)
- [Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection](https://mlsanity.com/paper/2004.04725/) (2020)
- [Self-produced Guidance for Weakly-supervised Object Localization](https://mlsanity.com/paper/1807.08902/) (2018)
- [Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up](https://mlsanity.com/paper/1903.02827/) (2019)
- [Cross-X Learning for Fine-Grained Visual Categorization](https://mlsanity.com/paper/1909.04412/) (2019)
- [Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition](https://mlsanity.com/paper/1806.05372/) (2018)
- [See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification](https://mlsanity.com/paper/1901.09891/) (2019)
- [Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection](https://mlsanity.com/paper/2010.12023/) (2020)

## Built on by (most cited first, 11 of 11)

- [Take 5: Interpretable Image Classification with a Handful of Features](https://mlsanity.com/paper/2303.13166/) (2023)
- [Car-1000: A New Large Scale Fine-Grained Visual Categorization Dataset](https://mlsanity.com/paper/2503.12385/) (2025)
- [Saccadic Vision for Fine-Grained Visual Classification](https://mlsanity.com/paper/2509.15688/) (2025)
- [Conviformers: Convolutionally guided Vision Transformer](https://mlsanity.com/paper/2208.08900/) (2022)
- [Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes](https://mlsanity.com/paper/2406.19814/) (2024)
- [Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset](https://mlsanity.com/paper/2408.13542/) (2024)
- [Semantic Feature Integration network for Fine-grained Visual Classification](https://mlsanity.com/paper/2302.10275/) (2023)
- [Leveraging Vision-Language Foundation Models for Fine-Grained Downstream Tasks](https://mlsanity.com/paper/2307.06795/) (2023)
- [Navigating limitations with precision: A fine-grained ensemble approach to wrist pathology recognition on a limited x-ray dataset](https://mlsanity.com/paper/2412.13884/) (2024)
- [Combining Discrepancy-Confusion Uncertainty and Calibration Diversity for Active Fine-Grained Image Classification](https://mlsanity.com/paper/2509.24181/) (2025)
- [Thinking Beyond Labels: Vocabulary-Free Fine-Grained Recognition using Reasoning-Augmented LMMs](https://mlsanity.com/paper/2512.18897/) (2025)

## Abstract (arXiv)

Visual classification can be divided into coarse-grained and fine-grained classification. Coarse-grained classification represents categories with a large degree of dissimilarity, such as the classification of cats and dogs, while fine-grained classification represents classifications with a large degree of similarity, such as cat species, bird species, and the makes or models of vehicles. Unlike coarse-grained visual classification, fine-grained visual classification often requires professional experts to label data, which makes data more expensive. To meet this challenge, many approaches propose to automatically find the most discriminative regions and use local features to provide more precise features. These approaches only require image-level annotations, thereby reducing the cost of annotation. However, most of these methods require two- or multi-stage architectures and cannot be trained end-to-end. Therefore, we propose a novel plug-in module that can be integrated to many common backbones, including CNN-based or Transformer-based networks to provide strongly discriminative regions. The plugin module can output pixel-level feature maps and fuse filtered features to enhance fine-grained visual classification. Experimental results show that the proposed plugin module outperforms state-of-the-art approaches and significantly improves the accuracy to 92.77% and 92.83% on CUB200-2011 and NABirds, respectively. We have released our source code in Github https://github.com/chou141253/FGVC-PIM.git.