细粒度分类，比赛，和相关模型介绍持续更新

2026/16更新 知乎专栏相关（细粒度）

https://zhuanlan.zhihu.com/fgva-cs

FGVC6 Workshop 共有十个挑战赛，每个都代表了细粒度视觉分类在某个细分领域的挑战

CVPR 2019 Cassava Disease Classification 比赛

2019 国内发表的一些好的领域的算法介绍
https://rrl360.com/boke/35114

1、2017鸟类的细粒度分类，粗到细的一个过程，加入了不同的损失
http://openaccess.thecvf.com/content_cvpr_2017/papers/Fu_Look_Closer_to_CVPR_2017_paper.pdf
代码还是有的
https://github.com/Jianlong-Fu/Recurrent-Attention-CNN/issues
https://pan.baidu.com/s/1Gx8ubL__Thc5LYTlX5hreA #网盘下载

2、2019京东蝶类精细度分类。

2019CVPR细粒度论文笔记《Destruction and Construction Learning for Fine-grained Image Recognition》
https://blog.csdn.net/zsx1713366249/article/details/92370490

2019-2020细腻度分类

1、A Systematic Evaluation:Fine-Grained CNN vs. Traditional CNN Classifiers 2020

主要对比分类模型，和细腻度模型方法，对细腻度数据的测试比对
通常的分类模型，densnet 比起最好的细腻度模型在细腻度数据上，效果更好一点

2 TIP2020 | 基于通道交互损失函数的细粒度图像分类方法

和上一篇正好测试结果互补，DCLresnet50,TASN ,MC_loss,Densenet161, NTS_net
最好。
https://github.com/dongliangchang/Mutual-Channel-Loss

3、BBN,BBN for Long-Tailed Visual Recognition(数据不平衡的训练）

特征提取模型和全连接模型分成两部分训练

文章首先指出了现在解决Long-Tail问题常用的re-balancing方法（Re-Weighting & Re-Sampling）虽然可以通过提升网络结构中classifier部分的性能，但是却损害了Representation部分（feature extractor backbone）学习的效果。如图1所示，虽然在Re-blancing之后，模型可以正确分类部分tail类的数据，但是却使得每个类的类内分布变得更加separable。

2017- 2019 细腻度分心代码和论文

ICCV2017 Dynamic Computational Time for Recurrent Attention Model (DT-RAM)

https://github.com/baidu-research/DT-RAM（Torch实现，ResNet的baseline很牛，CUB-200-2011数据集 84.5%，论文方法 86.0%）
http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w18/Li_Dynamic_Computational_Time_ICCV_2017_paper.pdf

ECCV2018 Learning to Navigate for Fine-grained Classification

https://github.com/yangze0930/NTS-Net（Pytorch实现，CUB-200-2011数据集 87.6%）
http://openaccess.thecvf.com/content_ECCV_2018/papers/Ze_Yang_Learning_to_Navigate_ECCV_2018_paper.pdf

CVPR 2018 Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition

https://github.com/songdejia/DFL-CNN（Pytorch第三方实现，值得借鉴，但无法复现原文）
https://arxiv.org/abs/1611.09932（CUB-200-2011数据集 87.4%）

ECCV 2018 Pairwise Confusion for Fine-Grained Visual Classification

http://openaccess.thecvf.com/content_ECCV_2018/papers/Abhimanyu_Dubey_Improving_Fine-Grained_Visual_ECCV_2018_paper.pdf

ECCV 2018 Multi-Attention Multi-Class Constraint for Fine-grained Image Recognition

http://openaccess.thecvf.com/content_ECCV_2018/papers/Ming_Sun_Multi-Attention_Multi-Class_Constraint_ECCV_2018_paper.pdf

ECCV 2018 Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

http://openaccess.thecvf.com/content_ECCV_2018/papers/Chaojian_Yu_Hierarchical_Bilinear_Pooling_ECCV_2018_paper.pdf

ECCV 2018 Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification

http://openaccess.thecvf.com/content_ECCV_2018/papers/Xing_Wei_Grassmann_Pooling_for_ECCV_2018_paper.pdf

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CVPR 2019 Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification From the Bottom Up

http://openaccess.thecvf.com/content_CVPR_2019/papers/Ge_Weakly_Supervised_Complementary_Parts_Models_for_Fine-Grained_Image_Classification_From_CVPR_2019_paper.pdf（CUB-200-2011数据集 90.4%，用了比较多的策略：+ Stacked LSTM + Multi-Loss，Baseline 82.8%）

CVPR 2019 Looking for the Devil in the Details: Learning Trilinear Attention Sampling Network for Fine-Grained Image Recognition

https://github.com/researchmm/tasn（MXNet实现，CUB-200-2011数据集 87.0%）
http://openaccess.thecvf.com/content_CVPR_2019/papers/Zheng_Looking_for_the_Devil_in_the_Details_Learning_Trilinear_Attention_CVPR_2019_paper.pdf

CVPR 2019 Destruction and Construction Learning for Fine-Grained Image Recognition

https://github.com/JDAI-CV/DCL（Pytorch实现，CUB-200-2011数据集 87.8%）
http://openaccess.thecvf.com/content_CVPR_2019/papers/Chen_Destruction_and_Construction_Learning_for_Fine-Grained_Image_Recognition_CVPR_2019_paper.pdf

ArXiv 2019 See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

https://github.com/tau-yihouxiang/WS_DAN（Tensorflow，官方实现）
https://github.com/GuYuc/WS-DAN.PyTorch（Pytorch，第三方实现）
https://arxiv.org/abs/1901.09891

https://zhuanlan.zhihu.com/p/94799831

2018 Learning a Discriminative Filter Bank within a CNN for Fine-grained Recognition，细粒度图像识别

来源，ssd 启发
https://blog.csdn.net/KongCDY/article/details/100009420