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arXiv · 1607.08764

SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition

Ravi Kiran Sarvadevabhatla·Shiv Surya·Srinivas S S Kruthiventi·Venkatesh Babu R
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Abstract

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.

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Ravi Kiran Sarvadevabhatla, Shiv Surya, Srinivas S S Kruthiventi, Venkatesh Babu R. 2016-07-29. SwiDeN : Convolutional Neural Networks For Depiction Invariant Object Recognition. https://arxiv.org/abs/1607.08764

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