Over the last couple of years few-shot learning (FSL) has attracted great attention towards minimizing the dependency on labeled training examples. An inherent difficulty in FSL is the handling of ambiguities resulting from having too few training samples per class. To tackle this fundamental challenge in FSL, we aim to train meta-learner models that can leverage prior semantic knowledge about novel classes to guide the classifier synthesis process. In particular, we propose semantically-conditioned feature attention and sample attention mechanisms that estimate the importance of representation dimensions and training instances. We also study the problem of sample noise in FSL, towards the utilization of meta-learners in more realistic and imperfect settings. Our experimental results demonstrate the effectiveness of the proposed semantic FSL model with and without sample noise.

翻译：在过去的几年中，小样本学习（FSL）因能够最大限度地减少对标注训练样本的依赖而备受关注。FSL的一个固有难点在于处理因每个类别训练样本过少而产生的歧义性。为应对这一根本性挑战，我们旨在训练能够利用关于新类别的先验语义知识来指导分类器合成过程的元学习模型。具体而言，我们提出了基于语义条件的特征注意力机制和样本注意力机制，用于评估表示维度与训练实例的重要性。同时，我们还研究了FSL中样本噪声的问题，以使元学习器能适用于更真实、非理想的环境。我们的实验结果证明了所提出的语义FSL模型在有/无样本噪声场景下的有效性。