Enhancing Few-shot Image Classification with Cosine Transformer

• URL: http://arxiv.org/abs/2211.06828v3
• Date: Fri, 21 Jul 2023 16:54:18 GMT
• Title: Enhancing Few-shot Image Classification with Cosine Transformer
• Authors: Quang-Huy Nguyen, Cuong Q. Nguyen, Dung D. Le, Hieu H. Pham
• Abstract summary: Few-shot Cosine Transformer (FS-CT) is a relational map between supports and queries. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications.
• Score: 4.511561231517167
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper addresses the few-shot image classification problem, where the classification task is performed on unlabeled query samples given a small amount of labeled support samples only. One major challenge of the few-shot learning problem is the large variety of object visual appearances that prevents the support samples to represent that object comprehensively. This might result in a significant difference between support and query samples, therefore undermining the performance of few-shot algorithms. In this paper, we tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the relational map between supports and queries is effectively obtained for the few-shot tasks. The FS-CT consists of two parts, a learnable prototypical embedding network to obtain categorical representations from support samples with hard cases, and a transformer encoder to effectively achieve the relational map from two different support and query samples. We introduce Cosine Attention, a more robust and stable attention module that enhances the transformer module significantly and therefore improves FS-CT performance from 5% to over 20% in accuracy compared to the default scaled dot-product mechanism. Our method performs competitive results in mini-ImageNet, CUB-200, and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and few-shot configurations. We also developed a custom few-shot dataset for Yoga pose recognition to demonstrate the potential of our algorithm for practical application. Our FS-CT with cosine attention is a lightweight, simple few-shot algorithm that can be applied for a wide range of applications, such as healthcare, medical, and security surveillance. The official implementation code of our Few-shot Cosine Transformer is available at https://github.com/vinuni-vishc/Few-Shot-Cosine-Transformer

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