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 176200 of 353 papers

TitleStatusHype
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification0
FILM: How can Few-Shot Image Classification Benefit from Pre-Trained Language Models?0
Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentation0
Multi-Similarity Contrastive Learning0
Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot Policy ImitationCode0
SuSana Distancia is all you need: Enforcing class separability in metric learning via two novel distance-based loss functions for few-shot image classification0
Strong Baselines for Parameter Efficient Few-Shot Fine-tuning0
Boosting Few-Shot Text Classification via Distribution Estimation0
RotoGBML: Towards Out-of-Distribution Generalization for Gradient-Based Meta-Learning0
Understanding and Constructing Latent Modality Structures in Multi-modal Representation Learning0
Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive FilteringCode0
CMVAE: Causal Meta VAE for Unsupervised Meta-LearningCode0
CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-190
On the Efficacy of Differentially Private Few-shot Image ClassificationCode0
Explore the Power of Dropout on Few-shot Learning0
ProD: Prompting-To-Disentangle Domain Knowledge for Cross-Domain Few-Shot Image Classification0
Exploiting Category Names for Few-Shot Classification with Vision-Language Models0
SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image ClassificationCode0
KGTN-ens: Few-Shot Image Classification with Knowledge Graph EnsemblesCode0
Visual-Semantic Contrastive Alignment for Few-Shot Image Classification0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation0
MergedNET: A simple approach for one-shot learning in siamese networks based on similarity layersCode0
A Unified Framework with Meta-dropout for Few-shot Learning0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification0
PaLI: A Jointly-Scaled Multilingual Language-Image Model0
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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+BPA (transductive)Accuracy85.59Unverified
6PT+MAP+SF+SOT (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+SOT (transductive)Accuracy91.34Unverified
9PT+MAP+SF+BPA (transductive)Accuracy91.34Unverified
10AmdimNetAccuracy90.98Unverified