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Long-tail Learning
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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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Delving Deep into Simplicity Bias for Long-Tailed Image Recognition
Weight-guided class complementing for long-tailed image recognition
Revisiting Long-tailed Image Classification: Survey and Benchmarks with New Evaluation Metrics
Learning Prototype Classifiers for Long-Tailed Recognition
Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data
Learning Imbalanced Data with Vision Transformers
Leveraging Angular Information Between Feature and Classifier for Long-tailed Learning: A Prediction Reformulation Approach
Supervised Contrastive Learning on Blended Images for Long-tailed Recognition
LPT: Long-tailed Prompt Tuning for Image Classification
Generalized Parametric Contrastive Learning
A Continual Development Methodology for Large-scale Multitask Dynamic ML Systems
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition
Long-Tailed Classification of Thorax Diseases on Chest X-Ray: A New Benchmark Study
Balanced Contrastive Learning for Long-Tailed Visual Recognition
Invariant Feature Learning for Generalized Long-Tailed Classification
Bag of Tricks for Long-Tail Visual Recognition of Animal Species in Camera-Trap Images
Maximum Class Separation as Inductive Bias in One Matrix
Balanced Product of Calibrated Experts for Long-Tailed Recognition
Long-tailed Recognition by Learning from Latent Categories
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation
ELM: Embedding and Logit Margins for Long-Tail Learning
Nested Collaborative Learning for Long-Tailed Visual Recognition
Long-Tailed Recognition via Weight Balancing
Visual Prompt Tuning
Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition
Do Deep Networks Transfer Invariances Across Classes?
Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning
Long-Tailed Classification with Gradual Balanced Loss and Adaptive Feature Generation
Retrieval Augmented Classification for Long-Tail Visual Recognition
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition
Pure Noise to the Rescue of Insufficient Data: Improving Imbalanced Classification by Training on Random Noise Images
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification
The Majority Can Help The Minority: Context-rich Minority Oversampling for Long-tailed Classification
A Simple Long-Tailed Recognition Baseline via Vision-Language Model
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