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Few Shot Image Classification

Computer Vision ist eine Technologie, die Maschinen ermöglicht, Bilder und Videos zu interpretieren und zu verstehen. Ihr Ziel ist es, durch die Simulation des menschlichen Sehsystems die automatische Erkennung und Analyse komplexer Szenen zu erreichen. Diese Technologie wird in Bereichen wie der medizinischen Bildgebung, autonomen Fahrzeugen und Sicherheitsüberwachung eingesetzt, wodurch Effizienz und Genauigkeit erheblich gesteigert werden und die Entwicklung einer intelligenteren Gesellschaft gefördert wird.

Synthesised Classifier

Human (Amateur)

Caltech-256 5-way (1-shot)

Caltech-256 5-way (5-shot)

MergedNet-Concat

CIFAR-FS - 1-Shot Learning

CIFAR-FS - 5-Shot Learning

CIFAR-FS 5-way (1-shot)

PT+MAP+SF+SOT (transductive)

CIFAR-FS 5-way (5-shot)

PT+MAP+SF+SOT (transductive)

CIFAR100 5-way (1-shot)

CUB-200 - 0-Shot Learning

CUB-200-2011 - 0-Shot

Word CNN-RNN (DS-SJE Embedding)

CUB-200-2011 5-way (1-shot)

CUB-200-2011 5-way (5-shot)

EASY 3xResNet12 (transductive)

CUB 200 5-way 1-shot

PT+MAP+SF+BPA (transductive)

CUB 200 5-way 5-shot

PT+MAP+SF+SOT (transductive)

CUB 200 50-way (0-shot)

Prototypical Networks

Dirichlet CUB-200 (5-way, 1-shot)

Dirichlet CUB-200 (5-way, 5-shot)

Dirichlet Mini-Imagenet (5-way, 1-shot)

Dirichlet Mini-Imagenet (5-way, 5-shot)

Dirichlet Tiered-Imagenet (5-way, 1-shot)

Dirichlet Tiered-Imagenet (5-way, 5-shot)

FC100 5-way (1-shot)

FC100 5-way (10-shot)

FC100 5-way (5-shot)

Fewshot-CIFAR100 - 1-Shot Learning

Fewshot-CIFAR100 - 5-Shot Learning

Flowers-102 - 0-Shot

Word CNN-RNN (DS-SJE Embedding)

ImageNet - 0-Shot

DebiasPL (ResNet50)

ImageNet (1-shot)

ImageNet - 1-shot

ViT-MoE-15B (Every-2)

ImageNet - 10-shot

MAWS (ViT-6.5B)

ImageNet - 5-shot

ViT-MoE-15B (Every-2)

ImageNet-FS (1-shot, all)

ImageNet-FS (1-shot, novel)

ImageNet-FS (10-shot, all)

KGTN (ResNet-50)

ImageNet-FS (10-shot, novel)

ImageNet-FS (2-shot, all)

ImageNet-FS (2-shot, novel)

ImageNet-FS (5-shot, all)

KGTN-ens (ResNet-50, h+g, max)

ImageNet-FS (5-shot, novel)

iNaturalist 2018 - 1-shot

iNaturalist 2018 - 10-shot

iNaturalist 2018 - 5-shot

iNaturalist (227-way multi-shot)

SMAT (DINO-VIT-Base-16-224)

Meta-Dataset Rank

Mini-ImageNet - 1-Shot Learning

Mini-Imagenet 10-way (1-shot)

Transductive CNAPS + FETI

Mini-Imagenet 10-way (5-shot)

Transductive CNAPS + FETI

mini-ImageNet - 100-Way

Mini-Imagenet 20-way (1-shot)

Mini-Imagenet 20-way (5-shot)

Mini-Imagenet 5-way (1-shot)

PT+MAP+SF+BPA (transductive)

Mini-Imagenet 5-way (10-shot)

Mini-Imagenet 5-way (5-shot)

CAML [Laion-2b]

Mini-ImageNet-CUB 5-way (1-shot)

Mini-ImageNet-CUB 5-way (5-shot)

Mini-ImageNet to CUB - 5 shot learning

miniImagenet → CUB (5-way 1-shot)

miniImagenet → CUB (5-way 5-shot)

OMNIGLOT - 1-Shot, 1000 way

OMNIGLOT - 1-Shot, 20-way

OMNIGLOT - 1-Shot, 423 way

OMNIGLOT - 1-Shot, 5-way

OMNIGLOT - 5-Shot, 1000 way

OMNIGLOT - 5-Shot, 20-way

OMNIGLOT - 5-Shot, 423 way

OMNIGLOT - 5-Shot, 5-way

OMNIGLOT-EMNIST 5-way (1-shot)

OMNIGLOT-EMNIST 5-way (5-shot)

ORBIT Clean Video Evaluation

SimpleCNAPs + LITE

ORBIT Clutter Video Evaluation

Oxford 102 Flower

Stanford Cars 5-way (1-shot)

Stanford Cars 5-way (5-shot)

Stanford Dogs 5-way (1-shot)

Stanford Dogs 5-way (5-shot)

Synthesised Classifier

Tiered ImageNet 10-way (1-shot)

Transductive CNAPS + FETI

Tiered ImageNet 10-way (5-shot)

Transductive CNAPS + FETI

Tiered ImageNet 5-way (1-shot)

Tiered ImageNet 5-way (5-shot)

CAML [Laion-2b]