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:由于缺乏可用数据,深层次的学习无法很好地完成少见的学习任务,然而,人类可以迅速从极少数样本中学习新类别的特点,然而,以前的工作很少考虑如何模仿人类认知行为并将其应用于少见的学习中。本文介绍Gestalt心理学,将Gestalt-Guided图像理解方法介绍为少见的学习,并提议Gestalt-Guided图像理解方法,即GGGIU。我们在Gestalt心理学中提及整体原则和封闭法,我们设计了 " Comity-Guided图像理解 " 和 " Short-Guided图像理解 " 来提取图像特征。之后,将使用一个特征估计模块来估计图像的准确特征。广泛的实验表明,我们的方法可以在不进行再培训或微调的情况下有效而灵活地改进现有模型的性能。我们的代码在https://github.com/skingorz/GGGIU上发布。