论文 | long-tailed recognition typical paper list 整理

一 写在前面

未经允许,不得转载,谢谢~~

最近想看看long-tailed recognition是如何处理imbalanced dataset的,对查阅到比较有用的资料做了一个整理和记录。

二 overview

2.1 基本问题介绍

大多数我们用的benchmark都是类别均衡的(每个类别的标注样本数一致),但是事实上自然界中的物体很可能是一个类别均衡的分布,常见类别样本多,稀有类别样本少,更直观的解释可以看下面这张图。


long-tailed recognition解决的就是数据呈现这样长尾分布时候的识别问题。

2.2 资料推荐

这边推荐两个我觉得很不错的link

三 typical paper list

根据现有的四大类方法(re-sampling,re-weighting,transfer learning,else),综合根据以上的资料和文章的引用量,code开源等情况整理了以下list,供需要的同学使用~

3.1 re-sampling

通过影响样本采样频率来达到balance,又可以分为头部类别欠采样(under-sampling)和尾部类别过采样(over-sampling)两个细分类别。

paper:ICLR2020: Decoupling Representation and Classifier for Long-Tailed Recognition, ICLR 2020 (star300+)

paper:BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition,CVPR 2020 (star300+)

3.2 re-weighting

此类方法主要表现在分类loss上,对loss进行加权。

paper:Class-Balanced Loss Based on Effective Number of Samples,CVPR 2019 (star300+)

paper:Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss,NIPS 2019(star300+)

3.3 transfer learning

希望将知识从头部类迁移到尾部类别。

paper:Large-Scale Long-Tailed Recognition in an Open World,CVPR 2019 (star500+)

paper:Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective,CVPR 2020

paper:Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification,ECCV 2020

3.4 else

paper:Long-tailed Recognition by Routing Diverse Distribution-Aware Experts, arxiv 2020

paper:ResLT: Residual Learning for Long-tailed Recognition,arxiv2021

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