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 351–353 of 353 papers
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
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | SgVA-CLIP | Accuracy | 97.95 | — | Unverified |
| 2 | CAML [Laion-2b] | Accuracy | 96.2 | — | Unverified |
| 3 | P>M>F (P=DINO-ViT-base, M=ProtoNet) | Accuracy | 95.3 | — | Unverified |
| 4 | TRIDENT | Accuracy | 86.11 | — | Unverified |
| 5 | PT+MAP+SF+BPA (transductive) | Accuracy | 85.59 | — | Unverified |
| 6 | PT+MAP+SF+SOT (transductive) | Accuracy | 85.59 | — | Unverified |
| 7 | PEMnE-BMS* (transductive) | Accuracy | 85.54 | — | Unverified |
| 8 | PT+MAP (s+f) (transductive) | Accuracy | 84.81 | — | Unverified |
| 9 | BAVARDAGE | Accuracy | 84.8 | — | Unverified |
| 10 | EASY 3xResNet12 (transductive) | Accuracy | 84.04 | — | Unverified |
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | SgVA-CLIP | Accuracy | 98.72 | — | Unverified |
| 2 | CAML [Laion-2b] | Accuracy | 98.6 | — | Unverified |
| 3 | P>M>F (P=DINO-ViT-base, M=ProtoNet) | Accuracy | 98.4 | — | Unverified |
| 4 | TRIDENT | Accuracy | 95.95 | — | Unverified |
| 5 | BAVARDAGE | Accuracy | 91.65 | — | Unverified |
| 6 | PEMnE-BMS*(transductive) | Accuracy | 91.53 | — | Unverified |
| 7 | Transductive CNAPS + FETI | Accuracy | 91.5 | — | Unverified |
| 8 | PT+MAP+SF+SOT (transductive) | Accuracy | 91.34 | — | Unverified |
| 9 | PT+MAP+SF+BPA (transductive) | Accuracy | 91.34 | — | Unverified |
| 10 | AmdimNet | Accuracy | 90.98 | — | Unverified |