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

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Showing 226–250 of 353 papers

TitleStatusHype
Multi-scale Adaptive Task Attention Network for Few-Shot Learning—0
Multi-Similarity Contrastive Learning—0
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation—0
Transfer Learning on Manifolds via Learned Transport Operators—0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification—0
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis—0
Asymmetric Distribution Measure for Few-shot Learning—0
NeurIPS'22 Cross-Domain MetaDL competition: Design and baseline results—0
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models—0
Object-Level Representation Learning for Few-Shot Image Classification—0
Few-shot Image Classification with Multi-Facet Prototypes—0
Few-Shot Image Classification via Contrastive Self-Supervised Learning—0
Assessing two novel distance-based loss functions for few-shot image classification—0
Few-shot Image Classification based on Gradual Machine Learning—0
Ontology-based n-ball Concept Embeddings Informing Few-shot Image Classification—0
Few-Shot Image Classification and Segmentation as Visual Question Answering Using Vision-Language Models—0
Optimal allocation of data across training tasks in meta-learning—0
Optimized Generic Feature Learning for Few-shot Classification across Domains—0
Few-Shot Image Classification Along Sparse Graphs—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
PaLI: A Jointly-Scaled Multilingual Language-Image Model—0
Few-Shot Classification & Segmentation Using Large Language Models Agent—0
Partner-Assisted Learning for Few-Shot Image Classification—0
Few-Shot Action Recognition with Compromised Metric via Optimal Transport—0
p-Meta: Towards On-device Deep Model Adaptation—0
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All datasetsMini-Imagenet 5-way (1-shot)Mini-Imagenet 5-way (5-shot)Tiered ImageNet 5-way (5-shot)Tiered ImageNet 5-way (1-shot)CIFAR-FS 5-way (5-shot)CIFAR-FS 5-way (1-shot)CUB 200 5-way 1-shotCUB 200 5-way 5-shotFC100 5-way (1-shot)FC100 5-way (5-shot)Meta-DatasetOMNIGLOT - 1-Shot, 20-way

Benchmark Results

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