- Rethinking the Value of Labels for Improving Class-Imbalanced Learning (NeurIPS2020) [paper] [code]
- Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-tailed Classification (ECCV2020) [paper]
- Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier (ECCV2020) [paper]
- Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets (ECCV2020) [paper]
- The Devil is in Classification: A Simple Framework for Long-tail Instance Segmentation (ECCV2020) [paper]
- Feature Space Augmentation for Long-Tailed Data (ECCV2020) [paper]
- Deep Representation Learning on Long-Tailed Data: A Learnable Embedding Augmentation Perspective (CVPR2020) [paper]
- Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition (CVPR2020) [paper]
- Deep Representation Domain Balancing: Face Recognition on Long-Tailed Domains (CVPR2020) [paper]
- Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition From a Domain Adaptation Perspective (CVPR2020) [paper]
- BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition (CVPR2020) [paper]
- Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax (CVPR2020) [paper]
- Equalization Loss for Long-Tailed Object Recognition (CVPR2020) [paper]
- M2m: Imbalanced Classification via Major-to-Minor Translation (CVPR2020) [paper]
- Deep Generative Model for Robust Imbalance Classification (CVPR2020) [paper]
- Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels (CVPR2020) [paper]
- Decoupling Representation and Classifier for Long-Tailed Recognition (ICLR2020) [paper]
-
Notifications
You must be signed in to change notification settings - Fork 16
License
xialeiliu/Awesome-LongTailed-Recognition
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
About
No description, website, or topics provided.
Resources
License
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published