Current state of the art object recognition architectures achieve impressive performance but are typically specialized for a single depictive style (e.g. photos only, sketches only). In this paper, we present SwiDeN : our Convolutional Neural Network (CNN) architecture which recognizes objects regardless of how they are visually depicted (line drawing, realistic shaded drawing, photograph etc.). In SwiDeN, we utilize a novel `deep' depictive style-based switching mechanism which appropriately addresses the depiction-specific and depiction-invariant aspects of the problem. We compare SwiDeN with alternative architectures and prior work on a 50-category Photo-Art dataset containing objects depicted in multiple styles. Experimental results show that SwiDeN outperforms other approaches for the depiction-invariant object recognition problem.

本文介绍了 SwiDeN——一种基于卷积神经网络（CNN）的物体识别架构，它可以识别出以不同方式视觉呈现的对象。这种架构采用了一种新颖的“深度”视觉呈现样式切换机制，可以适当地解决问题的特异性和不变性。实验表明，SwiDeN在50个类别的照片 - 艺术数据集上的表现优于其他方法。

SwiDeN：卷积神经网络用于具有表现不变性的物体识别