Long-tail Learning
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Benchmarks
ImageNet-LT
CIFAR-100-LT (ρ=100)
CIFAR-10-LT (ρ=10)
iNaturalist 2018
CIFAR-100-LT (ρ=10)
Places-LT
CIFAR-10-LT (ρ=100)
CIFAR-100-LT (ρ=50)
MIMIC-CXR-LT
NIH-CXR-LT
COCO-MLT
VOC-MLT
CIFAR-10-LT (ρ=50)
ImageNet-GLT
EGTEA
CIFAR-10-LT (ρ=200)
CIFAR-100-LT (ρ=200)
CelebA-5
Lot-insts
mini-ImageNet-LT
Most implemented
Focal Loss for Dense Object Detection
Learning Transferable Visual Models From Natural Language Supervision
Class-Balanced Loss Based on Effective Number of Samples
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
Visual Prompt Tuning
Parametric Contrastive Learning
Papers
Beyond the Majority: Long-tail Imitation Learning for Robotic Manipulation
While generalist robot policies hold significant promise for learning diverse manipulation skills through imitation, their performance is often hindered by the long-tail distribution of training demonstrations. Policies …
Long-tail LearningSpatial ReasoningEfficient Long-Tail Learning in Latent Space by sampling Synthetic Data
Imbalanced classification datasets pose significant challenges in machine learning, often leading to biased models that perform poorly on underrepresented classes. With the rise of foundation models, recent research has …
parameter-efficient fine-tuningComputational EfficiencyLong-tail LearningGenerative Active Learning for Long-tail Trajectory Prediction via Controllable Diffusion Model
While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works addressed this by modifying model architectures…
Trajectory PredictionLong-tail LearningAutonomous DrivingActive LearningMitigating Spurious Correlations with Causal Logit Perturbation
Deep learning has seen widespread success in various domains such as science, industry, and society. However, it is acknowledged that certain approaches suffer from non-robustness, relying on spurious correlations for pr…
counterfactualLong-tail LearningMeta-LearningLIFT+: Lightweight Fine-Tuning for Long-Tail Learning
The fine-tuning paradigm has emerged as a prominent approach for addressing long-tail learning tasks in the era of foundation models. However, the impact of fine-tuning strategies on long-tail learning performance remain…
Data AugmentationLong-tail LearningImproving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition
Long-tail learning has garnered widespread attention and achieved significant progress in recent times. However, even with pre-trained prior knowledge, models still exhibit weaker generalization performance on tail class…
Long-tail LearningVisual Prompt Tuning