论文标题
通过卷积神经网络学习对象的中间特征
Learning Intermediate Features of Object Affordances with a Convolutional Neural Network
论文作者
论文摘要
我们与周围世界互动的能力依赖于能够推断对象负担得起的行动 - 通常称为负担。对象行动关联的神经机制是在视觉运动途径中实现的，其中有关视觉属性和动作的信息都集成到通用表示中。但是，在负担的情况下，阐明这些机制尤其具有挑战性，因为视觉特征和推断动作之间几乎没有任何一对一的映射。为了更好地理解能力的本质，我们培训了深层卷积神经网络（CNN），以认识到图像中的负担并学习基础功能或负担的维度。这种特征形成了可提供的一般表示的基本组成结构，然后可以根据人类神经数据进行测试。我们将这种代表性分析视为朝着更正式的说明人类感知和与环境相互作用的第一步。
Our ability to interact with the world around us relies on being able to infer what actions objects afford -- often referred to as affordances. The neural mechanisms of object-action associations are realized in the visuomotor pathway where information about both visual properties and actions is integrated into common representations. However, explicating these mechanisms is particularly challenging in the case of affordances because there is hardly any one-to-one mapping between visual features and inferred actions. To better understand the nature of affordances, we trained a deep convolutional neural network (CNN) to recognize affordances from images and to learn the underlying features or the dimensionality of affordances. Such features form an underlying compositional structure for the general representation of affordances which can then be tested against human neural data. We view this representational analysis as the first step towards a more formal account of how humans perceive and interact with the environment.
