Balanced learning with Token Selection for Few-shot Classification
Keywords: few-shot classification, self-supervised learning, deep learning
Abstract: In recent years, patch-based approaches have shown promise in few-shot learning, with further improvements observed through the use of self-supervised learning.
However, we observe that the mainstream object-oriented approach focuses mainly on the salient part of the subject and also ignores the non-annotated part of the image.
Based on the assumption that any patch of the image is beneficial to learning, we present an end-to-end learning framework, which reconsiders the whole image from a multi-level perspective.
The learning of annotated subjects involves Direct Patch Learning (DPL) to promote balanced learning of different features, and Gaussian Mixup (GMIX) to provide extra mixed patch-level labels.
As for the non-annotated part, we utilize a cascading token selection strategy along with self-supervised learning to better utilize knowledge in the background in the current context by learning the consistent representation of different views from the same image.
Finally, in inductive few-shot learning, our method outperforms many previous methods and achieves new state-of-the-art performance.
Furthermore, it provides an insight that non-annotated parts still is favorable for few-shot learning.
As an ablation study, the effectiveness of each designed component is verified and the mechanism of how our method outperforms the baseline is shown both quantitatively and visually.
Primary Area: transfer learning, meta learning, and lifelong learning
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Submission Number: 5063
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