Long-tail Learning

37 papers with code • 11 benchmarks • 9 datasets

Long-tailed learning, one of the most challenging problems in visual recognition, aims to train well-performing models from a large number of images that follow a long-tailed class distribution.

Most implemented papers

Focal Loss for Dense Object Detection

facebookresearch/detectron ICCV 2017

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

Class-Balanced Loss Based on Effective Number of Samples

richardaecn/class-balanced-loss CVPR 2019

We design a re-weighting scheme that uses the effective number of samples for each class to re-balance the loss, thereby yielding a class-balanced loss.

Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

kaidic/LDAM-DRW NeurIPS 2019

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-imbalance but the testing criterion requires good generalization on less frequent classes.

ResLT: Residual Learning for Long-tailed Recognition

jiequancui/ResLT 26 Jan 2021

From this perspective, the trivial solution utilizes different branches for the head, medium, and tail classes respectively, and then sums their outputs as the final results is not feasible.

Parametric Contrastive Learning

jiequancui/Parametric-Contrastive-Learning ICCV 2021

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition.

Decoupling Representation and Classifier for Long-Tailed Recognition

facebookresearch/classifier-balancing ICLR 2020

The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem.

Improving Calibration for Long-Tailed Recognition

Jia-Research-Lab/MiSLAS CVPR 2021

Motivated by the fact that predicted probability distributions of classes are highly related to the numbers of class instances, we propose label-aware smoothing to deal with different degrees of over-confidence for classes and improve classifier learning.

Class-Balanced Distillation for Long-Tailed Visual Recognition

google-research/google-research 12 Apr 2021

An effective and simple approach to long-tailed visual recognition is to learn feature representations and a classifier separately, with instance and class-balanced sampling, respectively.

Large-Scale Long-Tailed Recognition in an Open World

zhmiao/OpenLongTailRecognition-OLTR CVPR 2019

We define Open Long-Tailed Recognition (OLTR) as learning from such naturally distributed data and optimizing the classification accuracy over a balanced test set which include head, tail, and open classes.

Long-tail learning via logit adjustment

google-research/google-research ICLR 2021

Real-world classification problems typically exhibit an imbalanced or long-tailed label distribution, wherein many labels are associated with only a few samples.