Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach outperforming the baseline model by 10% in 1-shot and around 5% in 5-shot approaches. Additionally, both visual-only and visual + textual generators have also been tested in this paper. The code is publicly available at https://github.com/heethanjan/Feature-Generator-for-FSL.

翻译：小样本学习（FSL）旨在使模型能够利用有限的标注数据识别新物体或类别。特征生成器通过合成新的数据点来扩增有限的数据集，已成为应对这一挑战的有效方案。本文研究了特征生成器在增强FSL任务嵌入过程中的有效性。针对每类图像稀缺导致的嵌入不准确问题，我们提出了一种从类别级文本描述生成视觉特征的特征生成器。通过结合分类器损失、判别器损失以及生成特征与真实类别嵌入之间的距离损失来训练生成器，我们确保了生成准确同类特征的能力，并提升了整体特征表示。实验结果表明，我们的方法在准确率上相比基线方法有显著提升，在1-shot设置中优于基线模型10%，在5-shot设置中提升约5%。此外，本文还测试了纯视觉生成器以及视觉+文本生成器。代码已公开于 https://github.com/heethanjan/Feature-Generator-for-FSL。