Dec 6, 2021 · Abstract:Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, ...

Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel un- seen classes, each represented by few ...

We apply Label-Halluc (our method pretrained w/ SKD) on 10-way 5-shot and 20-way 10-shot classification problems on FC-100, CIFAR-FS and miniImageNet. We use a ...

➡. Our method assigns novel-class labels to base images that match the few-shot examples in terms of background, shape, spatial layout, color, or texture.

The most common approach is to fine-tune a new classifier based on support-set S during the testing process, and the prediction process is similar to the ...

Oct 22, 2024 · Few-shot classification requires adapting knowledge learned from a large annotated base dataset to recognize novel unseen classes, ...

Dec 6, 2021 · This paper pseudo-labels the entire large dataset using the linear classifier trained on the novel classes, and finetunes the entire model ...

Our tensor feature hallucinator (TFH) sets new state of the art on three common few-shot classification bench- marks: miniImagenet, CUB and CIFAR-FS. 4. We ...

Aug 2, 2023 · The proposed approach outperforms state-of-the-art methods with 0.8% to 4.08% increases in classification accuracy for 5-way 5-shot tasks.

Dec 13, 2021 · Bibliographic details on Label Hallucination for Few-Shot Classification.