Due to the scarcity of available data, deep learning does not perform well on few-shot learning tasks. However, human can quickly learn the feature of a new category from very few samples. Nevertheless, previous work has rarely considered how to mimic human cognitive behavior and apply it to few-shot learning. This paper introduces Gestalt psychology to few-shot learning and proposes Gestalt-Guided Image Understanding, a plug-and-play method called GGIU. Referring to the principle of totality and the law of closure in Gestalt psychology, we design Totality-Guided Image Understanding and Closure-Guided Image Understanding to extract image features. After that, a feature estimation module is used to estimate the accurate features of images. Extensive experiments demonstrate that our method can improve the performance of existing models effectively and flexibly without retraining or fine-tuning. Our code is released on https://github.com/skingorz/GGIU.

翻译：由于可用数据稀缺，深度学习在小样本学习任务上表现不佳。然而，人类能够从极少量样本中快速学习新类别的特征。尽管如此，先前的工作很少考虑如何模仿人类认知行为并将其应用于小样本学习。本文将格式塔心理学引入小样本学习，提出一种即插即用的方法——格式塔引导的图像理解（GGIU）。参照格式塔心理学的整体性原则和闭合律，我们设计了整体引导的图像理解模块和闭合引导的图像理解模块以提取图像特征，随后使用特征估计模块估算图像的精确特征。大量实验表明，我们的方法无需重新训练或微调，即可有效且灵活地提升现有模型的性能。我们的代码已发布于https://github.com/skingorz/GGIU。