Long-Tailed-Visual-Recognition-Paper-List

[ImageNet-LT Paper with Code]

Arxiv and others

• [-] Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
• [-] You Only Need End-to-End Training for Long-Tailed Recognition ^
• [-] Margin Calibration for Long-Tailed Visual Recognition
• [-] Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images ^
• [-] A Simple Long-Tailed Recognition Baseline via Vision-Language Model *
• [-] VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition *
• [√] Deep Long-Tailed Learning: A Survey

CVPR2022

• [-] Long-tail Recognition via Compositional Knowledge Transfer
• [-] Targeted Supervised Contrastive Learning for Long-Tailed Recognition
• [-] Nested Collaborative Learning for Long-Tailed Visual Recognition [code]
• [-] BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning [code]
• [√] [*] Long-Tailed Recognition via Weight Balancing [code]
• [-] Balanced MSE for Imbalanced Visual Regression
• [√] Balanced Contrastive Learning for Long-Tailed Visual Recognition
• [√] Long-tail Recognition via Compositional Knowledge Transfer
• [-] The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification
• [-] Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment
• [-] A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty
• [-] Retrieval Augmented Classification for Long-Tail Visual Recognition

AAAI2022

• [√] Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification (TA) [code]
• [-] Memory-based Jitter: Improving Visual Recognition on Long-tailed Data with Diversity In Memory

BMVC2021

• [-] Class-Balanced Distillation for Long-Tailed Visual Recognition

NeurIPS 2021

• [-] Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
• [-] Topology-Imbalance Learning for Semi-Supervised Node Classification
• [√] [*] Towards Calibrated Model for Long-tailed Visual Recognition from Prior Perspective [code]
• [-] ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

ICCV 2021

• [√] Influence-Balanced Loss for Imbalanced Visual Classification (IB loss)
• [√] [*] Parametric Contrastive Learning (PaCo) [code]
• [√] [*] Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision (TADE) * (TA) [code]
• [-] Distilling Virtual Examples for Long-tailed Recognition
• [-] VideoLT: Large-scale Long-tailed Video Recognition
• [-] Ace: Ally complementary experts for solving long-tailed recognition in one-shot.

CVPR 2021

• [√] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition [code]
• [√] [*] Improving Calibration for Long-Tailed Recognition (MASLIS) [code]
• [√] Disentangling Label Distribution for Long-tailed Visual Recognition (LDAE) [code]
• [-] PML: Progressive Margin Loss for Long-tailed Age Classification
• [-] Distribution Alignment: A Unified Framework for Long-tail Visual Recognition [code]
• [√] Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
• [-] Distributional Robustness Loss for Long-tail Learning [code]

TPAMI

• [√] Regularizing Deep Networks with Semantic-Data Augmentation

WACV2021

• [√] From Generalized zero-shot learning to long-tail with class descriptors

AAAI 2021

• [√] [*] Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks [code]
• [√] ResLT: Residual Learning for Long-tailed Recognition

ICLR 2021

• [√] [*] Long-Tail Learning via Logit Adjustment [code]
• [-] Natural World Distribution via Adaptive Confusion Energy Regularization
• [-] EXPLORING BALANCED FEATURE SPACES FOR REPRESENTATION LEARNING

NIPS 2020

• [√] Balanced Meta-Softmax for Long-Tailed Visual Recognition [code]
• [√] Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect [code]
• [√] Rethinking the Value of Labels for Improving Class-Imbalanced Learning [code]
• [√] Identifying and Compensating for Feature Deviation in Imbalanced Deep Learning

ECCV 2020

• Distribution-Balanced Loss for Multi-Label Classification in Long-Tailed Datasets
• [√] Feature Space Augmentation for Long-Tailed Data
• [-] Learning From Multiple Experts- Self-paced Knowledge Distillation for Long-tailed Classification
• [-] Solving Long-tailed Recognition with Deep Realistic Taxonomic Classifier
• [√] The Devil is in Classification- A Simple Framework for Long-tail Instance Segmentation
• [√] Remix: Rebalanced Mixup

GCPR 2020

• [√] Long-Tailed Recognition Using Class-Balanced Experts

CVPR 2020

• [√] BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition
• [-] Deep Representation Learning on Long-tailed Data - A Learnable Embedding Augmentation Perspective
• [-] Deep Generative Model for Robust Imbalance Classification
• [-] Domain Balancing- Face Recognition on Long-Tailed Domains
• [√] ELF- An Early-Exiting Framework for Long-Tailed Classification
• [√] Equalization Loss for Long-Tailed Object Recognition
• [√] Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax
• [√] Inflated Episodic Memory with Region Self-Attention for Long-Tailed Visual Recognition
• [-] Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
• [√] M2m: Imbalanced Classification via Major-to-minor Translation
• [√] Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective

ICCV2020

• [-] Range Loss for Deep Face Recognition with Long-Tailed Training Data

CIKM2020

• [-] Large Scale Long-tailed Product Recognition System at Alibaba

ICLR 2020

• [√] [*] DECOUPLING REPRESENTATION AND CLASSIFIER FOR LONG-TAILED RECOGNITION
• EXTREME CLASSIFICATION VIA ADVERSARIAL SOFTMAX APPROXIMATION

NIPS 2019

• [√] [*] Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss

CVPR 2019

• [√] [*] Class-Balanced Loss Based on Effective Number of Samples
• [√] Libra R-CNN: Towards Balanced Learning for Object Detection
• [√] Large-Scale Long-Tailed Recognition in an Open World
• [√] Striking the Right Balance with Uncertainty
• [√] AdaptiveFace: Adaptive Margin and Sampling for Face Recognition

Others

• [√] Focal Loss for Dense Object Detection
• [√] The iNaturalist Species Classification and Detection Dataset
• [√] LVIS: A Dataset for Large Vocabulary Instance Segmentation
• [√] Gradient Harmonized Single-Stage Detector
• [√] Learning to Model the Tail
• [√] Improving Negative Sampling for Word Representation using Self-embedded Features

*：indicates high perferance.

^：indicates weird perferance.

TA: indicates Test-Agnostic.