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少样本图像分类
Few-Shot Image Classification 是一种计算机视觉任务，旨在利用少量标注样本（通常少于6个）训练机器学习模型，使其能够对新图像进行分类。该任务的目标是通过最小化监督和数据需求，实现模型对新类别的快速识别与分类，从而在有限的数据条件下提升模型的泛化能力。这种技术在实际应用中具有重要价值，特别是在数据获取困难或成本高昂的场景下。
Mini-Imagenet 5-way (1-shot)
PEMnE-BMS* (transductive)
Mini-Imagenet 5-way (5-shot)
CAML [Laion-2b]
Tiered ImageNet 5-way (5-shot)
CAML [Laion-2b]
Tiered ImageNet 5-way (1-shot)
PT+MAP
CIFAR-FS 5-way (5-shot)
PT+MAP+SF+SOT (transductive)
CIFAR-FS 5-way (1-shot)
PT+MAP+SF+SOT (transductive)
CUB 200 5-way 1-shot
PT+MAP+SF+BPA (transductive)
CUB 200 5-way 5-shot
PT+MAP+SF+SOT (transductive)
FC100 5-way (1-shot)
R2-D2+Task Aug
FC100 5-way (5-shot)
Meta-Dataset
URT
OMNIGLOT - 1-Shot, 20-way
GCR
OMNIGLOT - 5-Shot, 20-way
MC2+
OMNIGLOT - 1-Shot, 5-way
MC2+
OMNIGLOT - 5-Shot, 5-way
DCN6-E
Mini-ImageNet - 1-Shot Learning
PT+MAP
Mini-Imagenet 10-way (5-shot)
Transductive CNAPS + FETI
Mini-Imagenet 10-way (1-shot)
Transductive CNAPS + FETI
Tiered ImageNet 10-way (5-shot)
Transductive CNAPS + FETI
Meta-Dataset Rank
URT
Tiered ImageNet 10-way (1-shot)
Transductive CNAPS + FETI
Dirichlet Mini-Imagenet (5-way, 1-shot)
alpha-TIM
Dirichlet Mini-Imagenet (5-way, 5-shot)
alpha-TIM
Mini-ImageNet-CUB 5-way (1-shot)
PT+MAP
Dirichlet Tiered-Imagenet (5-way, 5-shot)
alpha-TIM
Dirichlet Tiered-Imagenet (5-way, 1-shot)
Mini-ImageNet-CUB 5-way (5-shot)
PT+MAP
Dirichlet CUB-200 (5-way, 5-shot)
ImageNet - 5-shot
ViT-MoE-15B (Every-2)
ImageNet - 1-shot
ViT-MoE-15B (Every-2)
Dirichlet CUB-200 (5-way, 1-shot)
ImageNet-FS (2-shot, novel)
ImageNet-FS (5-shot, all)
KGTN-ens (ResNet-50, h+g, max)
ImageNet-FS (1-shot, novel)
Bongard-HOI
Human (Amateur)
ImageNet - 10-shot
ViT-MoE-15B (Every-2)
Stanford Cars 5-way (5-shot)
MATANet
Mini-Imagenet 20-way (1-shot)
TIM-GD
Mini-Imagenet 20-way (5-shot)
TIM-GD
Stanford Cars 5-way (1-shot)
MATANet
Stanford Dogs 5-way (5-shot)
ImageNet - 0-Shot
CLIP (ViT B/32)
Mini-Imagenet 5-way (10-shot)
PT+MAP
CUB-200-2011 - 0-Shot
Word CNN-RNN (DS-SJE Embedding)
CUB 200 50-way (0-shot)
Prototypical Networks
Stanford Dogs 5-way (1-shot)
MATANet
CUB-200 - 0-Shot Learning
TAFE-Net
Caltech-256 5-way (1-shot)
ImageNet-FS (5-shot, novel)
ORBIT Clutter Video Evaluation
ProtoNetsVideo
ImageNet-FS (10-shot, all)
KGTN (ResNet-50)
SUN - 0-Shot
Synthesised Classifier
ImageNet-FS (10-shot, novel)
ORBIT Clean Video Evaluation
SimpleCNAPs + LITE
Mini-ImageNet to CUB - 5 shot learning
TIM-GD
ImageNet-FS (1-shot, all)
OMNIGLOT-EMNIST 5-way (5-shot)
CIFAR100 5-way (1-shot)
ImageNet (1-shot)
OMNIGLOT-EMNIST 5-way (1-shot)
HyperShot
ImageNet-FS (2-shot, all)
iNaturalist 2018 - 10-shot
CIFAR-FS - 1-Shot Learning
pseudo-shots
mini-ImageNet - 100-Way
GCR
iNaturalist 2018 - 1-shot
OMNIGLOT - 5-Shot, 1000 way
CUB-200-2011 5-way (5-shot)
MATANet
OMNIGLOT - 1-Shot, 423 way
APL
iNaturalist 2018 - 5-shot
Caltech-256 5-way (5-shot)
MergedNet-Concat
AWA2 - 0-Shot
iNaturalist (227-way multi-shot)
LaplacianShot
aPY - 0-Shot
TAFE-Net
OMNIGLOT - 5-Shot, 423 way
APL
Caltech101
PRE
CIFAR-FS - 5-Shot Learning
pseudo-shots
OMNIGLOT - 1-Shot, 1000 way
APL
Fewshot-CIFAR100 - 1-Shot Learning
pseudo-shots
Flowers-102 - 0-Shot
Word CNN-RNN (DS-SJE Embedding)
miniImagenet → CUB (5-way 5-shot)
LaplacianShot
CUB-200-2011 5-way (1-shot)
MATANet
Oxford 102 Flower
RS-FSL
AWA - 0-Shot
Synthesised Classifier
AWA1 - 0-Shot
CUB 200 5-way
EASY 3xResNet12 (transductive)
UT Zappos50K
Fewshot-CIFAR100 - 5-Shot Learning
pseudo-shots
miniImagenet → CUB (5-way 1-shot)
LaplacianShot
FC100 5-way (10-shot)
MTL
CIFAR-100