Awesome-of-Long-Tailed-Recognition

A curated list of long-tailed recognition resources.
https://github.com/zzw-zwzhang/Awesome-of-Long-Tailed-Recognition

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Table of Contents
- 2021
- 2020
  - Rethinking the Value of Labels for Improving Class-Imbalanced Learning - semi-self) | `153` |
  - Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect - Tailed-Recognition.pytorch) |
  - Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation - MM | `Other` | [PyTorch(Author)](https://github.com/JialianW/Forest_RCNN) |
  - Mitigating Dataset Imbalance via Joint Generation and Classification - W | `Other` | [PyTorch(Author)](https://github.com/AadSah/ImbalanceCycleGAN) |
  - Seesaw Loss for Long-Tailed Instance - W | `Other` | - |
  - Balanced Activation for Long-tailed Visual Recognition - W | `Other` | - |
  - Imbalanced Continual Learning with Partitioning Reservoir Sampling
  - Feature Space Augmentation for Long-Tailed Data - |
  - Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
  - Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier - |
  - Learning From Multiple Experts_Self-paced Knowledge Distillation for Long-tailed Classification - |
  - Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective - |
  - Equalization Loss for Long-Tailed Object Recognition
  - Domain Balancing: Face Recognition on Long-Tailed Domains - |
  - BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition - Nanjing/BBN) | `360` |
  - Deep Representation Learning on Long-tailed Data: A Learnable Embedding - |
  - Inflated Episodic Memory with Region Self-Attention for Long-Tailed Visual Recognition - |
  - M2m: Imbalanced Classification via Major-to-minor Translation
  - Deep Generative Model for Robust Imbalance Classification
  - Learning to Segment the Tail - |
  - Decoupling Representation and Classifier for Long-Tailed Recognition - balancing) | `354` |
  - Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation - MM | `Other` | [PyTorch(Author)](https://github.com/JialianW/Forest_RCNN) |
  - Balanced Activation for Long-tailed Visual Recognition - W | `Other` | - |
  - Imbalanced Continual Learning with Partitioning Reservoir Sampling
  - Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
  - Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier - |
  - Learning From Multiple Experts_Self-paced Knowledge Distillation for Long-tailed Classification - |
  - Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective - |
  - Equalization Loss for Long-Tailed Object Recognition
  - Deep Representation Learning on Long-tailed Data: A Learnable Embedding - |
  - Overcoming Classiﬁer Imbalance for Long-tail Object Detection with Balanced Group Softmax
  - M2m: Imbalanced Classification via Major-to-minor Translation
  - Mitigating Dataset Imbalance via Joint Generation and Classification - W | `Other` | [PyTorch(Author)](https://github.com/AadSah/ImbalanceCycleGAN) |
  - Seesaw Loss for Long-Tailed Instance - W | `Other` | - |
  - Feature Space Augmentation for Long-Tailed Data - |
  - Overcoming Classiﬁer Imbalance for Long-tail Object Detection with Balanced Group Softmax
  - Learning to Segment the Tail - |
  - Rethinking the Value of Labels for Improving Class-Imbalanced Learning - semi-self) | `153` |
  - BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition - Nanjing/BBN) | `360` |
  - Decoupling Representation and Classifier for Long-Tailed Recognition - balancing) | `354` |
  - Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect - Tailed-Recognition.pytorch) |
- 2019
  - Class-Balanced Loss Based on Effective Number of Samples - balanced-loss) [PyTorch(3rd)](https://github.com/vandit15/Class-balanced-loss-pytorch) | `390/409` |
  - Striking the Right Balance with Uncertainty - |
  - Feature Transfer Learning for Face Recognition with Under-Represented Data
  - Large-Scale Long-Tailed Recognition in an Open World - OLTR) | `523` |
  - Unequal-training for Deep Face Recognition with Long-tailed Noisy Data - Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data) |
  - Learning for Tail Label Data: A Label-Specific Feature Approach - |
  - Dynamic Curriculum Learning for Imbalanced Data Classification - |
  - Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss - DRW) | `233` |
  - Meta-Weight-Net_Learning an Explicit Mapping for Sample Weighting - weight-net) [PyTorch(3rd)](https://github.com/robertcedergren/Meta-Weight-Net-Learning-an-Explicit-Mapping-For-Sample-Weighting) | `133/1` |
  - The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation - |
  - Class-Balanced Loss Based on Effective Number of Samples - balanced-loss) [PyTorch(3rd)](https://github.com/vandit15/Class-balanced-loss-pytorch) | `390/409` |
  - Striking the Right Balance with Uncertainty - |
  - Dynamic Curriculum Learning for Imbalanced Data Classification - |
  - Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss - DRW) | `233` |
  - Meta-Weight-Net_Learning an Explicit Mapping for Sample Weighting - weight-net) [PyTorch(3rd)](https://github.com/robertcedergren/Meta-Weight-Net-Learning-an-Explicit-Mapping-For-Sample-Weighting) | `133/1` |
  - The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation - |
  - Large-Scale Long-Tailed Recognition in an Open World - OLTR) | `523` |
- 2018
  - Large Scale Fine-Grained Categorization and Domain-Specific Transfer - inaturalist-transfer) | `146` |
  - Learning to Reweight Examples for Robust Deep Learning - research/learning-to-reweight-examples) [PyTorch(3rd)](https://github.com/danieltan07/learning-to-reweight-examples) | `188/222` |
  - Clustering and Learning from Imbalanced Data - W | `OS` | - |
  - Large Scale Fine-Grained Categorization and Domain-Specific Transfer - inaturalist-transfer) | `146` |
  - Clustering and Learning from Imbalanced Data - W | `OS` | - |
  - Learning to Reweight Examples for Robust Deep Learning - research/learning-to-reweight-examples) [PyTorch(3rd)](https://github.com/danieltan07/learning-to-reweight-examples) | `188/222` |
- 2017
- 2016
- Previous Venues
- arXiv
Imbalanced Learning
- arXiv
  - Learning from Imbalanced Data - |

Categories

Table of Contents 120 Imbalanced Learning 1

Sub Categories

2020 41 arXiv 26 2019 17 2021 15 2017 7 2018 6 Previous Venues 5 2016 4