SOTAVerified

Long-tail Learning

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.

Papers

Showing 1–50 of 131 papers

TitleStatusHype
Visual Prompt TuningCode3
Adaptive Parametric ActivationCode2
Probabilistic Contrastive Learning for Long-Tailed Visual RecognitionCode2
SURE: SUrvey REcipes for building reliable and robust deep networksCode2
Generalized Parametric Contrastive LearningCode2
BatchFormer: Learning to Explore Sample Relationships for Robust Representation LearningCode2
A Simple Episodic Linear Probe Improves Visual Recognition in the WildCode2
Learning Transferable Visual Models From Natural Language SupervisionCode2
Focal Loss for Dense Object DetectionCode2
Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual RecognitionCode1
Continuous Contrastive Learning for Long-Tailed Semi-Supervised RecognitionCode1
AUCSeg: AUC-oriented Pixel-level Long-tail Semantic SegmentationCode1
LTRL: Boosting Long-tail Recognition via Reflective LearningCode1
DeiT-LT Distillation Strikes Back for Vision Transformer Training on Long-Tailed DatasetsCode1
EAT: Towards Long-Tailed Out-of-Distribution DetectionCode1
Long-Tail Learning with Foundation Model: Heavy Fine-Tuning HurtsCode1
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed RecognitionCode1
Norm-guided latent space exploration for text-to-image generationCode1
On the Effectiveness of Out-of-Distribution Data in Self-Supervised Long-Tail LearningCode1
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual RecognitionsCode1
LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual RecognitionCode1
Long-Tailed Recognition by Mutual Information Maximization between Latent Features and Ground-Truth LabelsCode1
Predicting and Enhancing the Fairness of DNNs with the Curvature of Perceptual ManifoldsCode1
Text Classification in the Wild: a Large-scale Long-tailed Name Normalization DatasetCode1
CUDA: Curriculum of Data Augmentation for Long-Tailed RecognitionCode1
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed RecognitionCode1
Escaping Saddle Points for Effective Generalization on Class-Imbalanced DataCode1
Learning Imbalanced Data with Vision TransformersCode1
LPT: Long-tailed Prompt Tuning for Image ClassificationCode1
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed RecognitionCode1
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark StudyCode1
Balanced Contrastive Learning for Long-Tailed Visual RecognitionCode1
Invariant Feature Learning for Generalized Long-Tailed ClassificationCode1
Maximum Class Separation as Inductive Bias in One MatrixCode1
Balanced Product of Calibrated Experts for Long-Tailed RecognitionCode1
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated DistillationCode1
Nested Collaborative Learning for Long-Tailed Visual RecognitionCode1
Long-Tailed Recognition via Weight BalancingCode1
Do Deep Networks Transfer Invariances Across Classes?Code1
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?Code1
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise ImagesCode1
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed ClassificationCode1
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed ClassificationCode1
A Simple Long-Tailed Recognition Baseline via Vision-Language ModelCode1
Targeted Supervised Contrastive Learning for Long-Tailed RecognitionCode1
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual RecognitionCode1
Trustworthy Long-Tailed ClassificationCode1
Feature Generation for Long-tail ClassificationCode1
Towards Calibrated Model for Long-Tailed Visual Recognition from Prior PerspectiveCode1
Self-supervised Learning is More Robust to Dataset ImbalanceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LIFT (ViT-L/14)Top-1 Accuracy82.9—Unverified
2µ2Net+ (ViT-L/16)Top-1 Accuracy82.5—Unverified
3MAM (ViT-B/16)Top-1 Accuracy82.3—Unverified
4LIFT (ViT-B/16)Top-1 Accuracy78.3—Unverified
5VL-LTR (ViT-B-16)Top-1 Accuracy77.2—Unverified
6BALLAD(ResNet-50×16)Top-1 Accuracy76.5—Unverified
7BALLAD(ViT-B-16)Top-1 Accuracy75.7—Unverified
8BALLAD(ResNet-101)Top-1 Accuracy70.5—Unverified
9VL-LTR (ResNet-50)Top-1 Accuracy70.1—Unverified
10BALLAD(ResNet-50)Top-1 Accuracy67.2—Unverified
#ModelMetricClaimedVerifiedStatus
1Cross-Entropy (CE)Error Rate62.75—Unverified
2Cross-Entropy (CE)Error Rate61.68—Unverified
3IBLLossError Rate61.52—Unverified
4Cross-Entropy + Curvature RegularizationError Rate59.5—Unverified
5CE-DRWError Rate58.9—Unverified
6LDAM-DRWError Rate57.96—Unverified
7ELPError Rate57.6—Unverified
8CDB-lossError Rate57.43—Unverified
9CE-DRW-ICError Rate56.9—Unverified
10LDAM-DRW + SSPError Rate56.57—Unverified
#ModelMetricClaimedVerifiedStatus
1RISDAError Rate20.11—Unverified
2Empirical Risk Minimization (ERM, CE)Error Rate13.61—Unverified
3Class-balanced ReweightingError Rate13.46—Unverified
4Class-balanced ResamplingError Rate13.21—Unverified
5IBLLossError Rate12.93—Unverified
6Class-balanced Focal LossError Rate12.9—Unverified
7DecTDEError Rate12.63—Unverified
8M2mError Rate12.5—Unverified
9Prior-LTError Rate12.2—Unverified
10KCLError Rate12—Unverified
#ModelMetricClaimedVerifiedStatus
1LIFT (ViT-L/14@336px)Top-1 Accuracy87.4—Unverified
2LIFT (ViT-L/14)Top-1 Accuracy85.2—Unverified
3GML (ViT-B-16)Top-1 Accuracy82.1—Unverified
4LIFT (ViT-B/16)Top-1 Accuracy80.4—Unverified
5RAC (ViT-B-16)Top-1 Accuracy80.24—Unverified
6GPaCo (2-R152)Top-1 Accuracy79.8—Unverified
7TADE(ResNet-152)Top-1 Accuracy77—Unverified
8ProCo (ResNet50)Top-1 Accuracy75.8—Unverified
9MDCS(Resnet50)Top-1 Accuracy75.6—Unverified
10DeiT-LTTop-1 Accuracy75.1—Unverified