Enhancing Few-shot Image Classification with Cosine Transformer

TL;DR

This paper introduces FS-CT, a novel few-shot image classification method using a cosine attention transformer, significantly improving accuracy and robustness over existing approaches, with applications demonstrated on standard datasets and a new Yoga pose dataset.

Contribution

The paper proposes a lightweight, effective few-shot classification framework with a novel cosine attention mechanism, enhancing relational mapping between support and query samples.

Findings

Improves accuracy by 5% to over 20% over baseline methods.

Achieves competitive results on mini-ImageNet, CUB-200, and CIFAR-FS.

Demonstrates practical application on Yoga pose recognition dataset.

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