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[bibtex]@InProceedings{Bar_2024_CVPR, author = {B\"ar, Andreas and Houlsby, Neil and Dehghani, Mostafa and Kumar, Manoj}, title = {Frozen Feature Augmentation for Few-Shot Image Classification}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {16046-16057} }
Frozen Feature Augmentation for Few-Shot Image Classification
Abstract
Training a linear classifier or lightweight model on top of pretrained vision model outputs so-called 'frozen features' leads to impressive performance on a number of downstream few-shot tasks. Currently frozen features are not modified during training. On the other hand when networks are trained directly on images data augmentation is a standard recipe that improves performance with no substantial overhead. In this paper we conduct an extensive pilot study on few-shot image classification that explores applying data augmentations in the frozen feature space dubbed 'frozen feature augmentation (FroFA)' covering twenty augmentations in total. Our study demonstrates that adopting a deceptively simple pointwise FroFA such as brightness can improve few-shot performance consistently across three network architectures three large pretraining datasets and eight transfer datasets.
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