We propose Recognition as Part Composition (RPC), an image encoding approach
inspired by human cognition. It is based on the cognitive theory that humans
recognize complex objects by components, and that they build a small compact
vocabulary of concepts to represent each instance with. RPC encodes images by
first decomposing them into salient parts, and then encoding each part as a
mixture of a small number of prototypes, each representing a certain concept.
We find that this type of learning inspired by human cognition can overcome
hurdles faced by deep convolutional networks in low-shot generalization tasks,
like zero-shot learning, few-shot learning and unsupervised domain adaptation.
Furthermore, we find a classifier using an RPC image encoder is fairly robust
to adversarial attacks, that deep neural networks are known to be prone to.
Given that our image encoding principle is based on human cognition, one would
expect the encodings to be interpretable by humans, which we find to be the
case via crowd-sourcing experiments. Finally, we propose an application of
these interpretable encodings in the form of generating synthetic attribute
annotations for evaluating zero-shot learning methods on new datasets.

基于人类认知启发的 Recognition as Part Composition 图像编码方法，在零样本学习、少样本学习和无监督领域自适应等低样本泛化任务中，可以克服深度卷积神经网络面临的难题，并且在对抗攻击下比深度神经网络更具鲁棒性。此外，采用 RPC 图像编码器的分类器对人而言是可解释的。基于此，我们提出了一种应用解释性编码的方法，生成用于评估新数据集零样本学习方法的合成属性注释。