Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference

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Benchmark Model Rank Results
few-shot-image-classification-on-meta-datasetP>M>F (P=DINO-ViT-base, M=ProtoNet)#2Accuracy: 84.75
few-shot-image-classification-on-mini-imagenet-5-way-1-shotP>M>F (P=DINO-ViT-base, M=ProtoNet)#3Accuracy: 95.3
few-shot-image-classification-on-mini-imagenet-5-way-5-shotP>M>F (P=DINO-ViT-base, M=ProtoNet)#3Accuracy: 98.4
few-shot-image-classification-on-cifar-fs-5-way-5-shotP>M>F (P=DINO-ViT-base, M=ProtoNet)#4Accuracy: 92.2
few-shot-image-classification-on-cifar-fs-5-way-1-shotP>M>F (P=DINO-ViT-base, M=ProtoNet)#9Accuracy: 84.3

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