SOTAVerified

Few-Shot Image Classification

Few-Shot Image Classification is a computer vision task that involves training machine learning models to classify images into predefined categories using only a few labeled examples of each category (typically ( Image credit: Learning Embedding Adaptation for Few-Shot Learning )

Papers

Showing 76100 of 353 papers

TitleStatusHype
Charting the Right Manifold: Manifold Mixup for Few-shot LearningCode1
Class-Aware Patch Embedding Adaptation for Few-Shot Image ClassificationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image ClassificationCode1
Dynamic Few-Shot Visual Learning without ForgettingCode1
EASY: Ensemble Augmented-Shot Y-shaped Learning: State-Of-The-Art Few-Shot Classification with Simple IngredientsCode1
GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised LearningCode1
Generalized Few-Shot Video Classification with Video Retrieval and Feature GenerationCode1
Large Language Models are Good Prompt Learners for Low-Shot Image ClassificationCode1
Embedding Propagation: Smoother Manifold for Few-Shot ClassificationCode1
Empirical Bayes Transductive Meta-Learning with Synthetic GradientsCode1
Enhancing Few-Shot Image Classification through Learnable Multi-Scale Embedding and Attention MechanismsCode1
Enhancing Few-shot Image Classification with Cosine TransformerCode1
How to train your MAMLCode1
Meta-Transfer Learning for Few-Shot LearningCode1
ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill IdentificationCode1
ESPT: A Self-Supervised Episodic Spatial Pretext Task for Improving Few-Shot LearningCode1
HyperTransformer: Model Generation for Supervised and Semi-Supervised Few-Shot LearningCode1
Negative Margin Matters: Understanding Margin in Few-shot ClassificationCode1
Memory Efficient Meta-Learning with Large ImagesCode1
Improving ProtoNet for Few-Shot Video Object Recognition: Winner of ORBIT Challenge 2022Code1
Adaptive Subspaces for Few-Shot LearningCode1
Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot LearningCode1
Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive ProcessesCode1
ORBIT: A Real-World Few-Shot Dataset for Teachable Object RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy97.95Unverified
2CAML [Laion-2b]Accuracy96.2Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy95.3Unverified
4TRIDENTAccuracy86.11Unverified
5PT+MAP+SF+SOT (transductive)Accuracy85.59Unverified
6PT+MAP+SF+BPA (transductive)Accuracy85.59Unverified
7PEMnE-BMS* (transductive)Accuracy85.54Unverified
8PT+MAP (s+f) (transductive)Accuracy84.81Unverified
9BAVARDAGEAccuracy84.8Unverified
10EASY 3xResNet12 (transductive)Accuracy84.04Unverified
#ModelMetricClaimedVerifiedStatus
1SgVA-CLIPAccuracy98.72Unverified
2CAML [Laion-2b]Accuracy98.6Unverified
3P>M>F (P=DINO-ViT-base, M=ProtoNet)Accuracy98.4Unverified
4TRIDENTAccuracy95.95Unverified
5BAVARDAGEAccuracy91.65Unverified
6PEMnE-BMS*(transductive)Accuracy91.53Unverified
7Transductive CNAPS + FETIAccuracy91.5Unverified
8PT+MAP+SF+BPA (transductive)Accuracy91.34Unverified
9PT+MAP+SF+SOT (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified