Learning from limited supervision is a challenging problem that has recently attracted wide attention in the machine learning community. With scarce annotated samples available in target categories, so-called few-shot image recognition aims to transfer basic knowledge from a large-scale image set to recognize unseen classes. Many existing approaches tend to learn a general source data representation and apply it to address the few-shot task by building a target classifier on scare support features, which performs favorably only if source and target data distributions are similar. We argue that ignoring the distribution gap and directly leveraging frozen representations lead to a sub-optimal solution. Taking domain shift into consideration, we explore an efficient task adaptation strategy that can jointly achieve task and domain transfer. Accordingly, we propose a simple yet effective method, called proxy-based domain adaptation (PDA), to optimize the pre-trained representation and a target classifier simultaneously. PDA can be characterized as: (1) a source-data-independent approach that only leverages few support data from the target domain (2) a non-parametric adaptation method that performs model adaptation by minimizing a designed loss without involving any parametric modules additionally. We extensively conduct experiments on multiple few-shot image recognition benchmarks, highlighting the superiority of PDA over many SOTA methods. Besides, careful ablation studies verify each component's effectiveness in our method and demonstrate the significance of domain adaptation in few-shot image recognition.

从有限的监督中学习是一个具有挑战性的问题，最近在机器学习社区中引起了广泛的关注。在目标类别中有可用的带注释的样本稀缺的情况下，所谓的“少拍图像识别”旨在转移大规模图像集中的基础知识，以识别看不见的类别。许多现有方法倾向于学习通用的源数据表示形式，并通过在恐慌支持功能上构建目标分类器来解决一般任务，仅在源数据分布和目标数据分布相似时才能很好地执行。我们认为忽略分布差距并直接利用冻结的表示会导致次优解决方案。考虑到域转移，我们探索了一种有效的任务自适应策略，该策略可以共同实现任务和域转移。PDA），以同时优化预训练表示和目标分类器。PDA可以表征为：（1）仅利用源数据中很少的支持数据的独立于源数据的方法（2）一种非参数自适应方法，该方法通过使设计损失最小化来执行模型自适应，而无需另外使用任何参数模块。我们在多个镜头图像识别基准上进行了广泛的实验，突出了PDA在许多SOTA方法上的优越性。此外，认真的消融研究验证了我们方法中每个组件的有效性，并证明了域适应在少拍图像识别中的重要性。