用文字描述生成视觉特征，提升少样本学习准确率
A Feature Generator for Few-Shot Learning
- 根据类别文字描述生成视觉特征，弥补数据稀缺
- 1次学习提升10%，5次学习提升约5%准确率
- 适合需要小样本训练的图像识别场景
少样本学习（FSL）旨在仅用少量标注数据就识别新物体或类别。特征生成器通过合成新数据点来扩充有限数据集，成为应对该挑战的有前景方案。本文研究了特征生成器在增强FSL嵌入过程中的有效性。为解决每类图像过少导致的嵌入不准确问题，提出一种从类别级文本描述生成视觉特征的生成器。通过联合训练分类器损失、判别器损失及生成特征与真实类别嵌入间的距离损失，确保生成同类别特征的准确性并提升整体特征表示。实验结果表明，该方法在1次学习下较基线模型提升10%，在5次学习下提升约5%。同时测试了仅视觉和视觉+文本两种生成器。代码已公开于https://github.com/heethanjan/Feature-Generator-for-FSL。
原文摘要 · Abstract (English)
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.
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