Occluded Visual Object Recognition Using Deep Conditional Generative Adversarial Nets and Feedforward Convolutional Neural Networks

Abstract

Core object recognition is the task of recognizing objects without regard to any variations in the conditions like pose, illumination or any other structural modifications. This task is solved through the feedforward processing of information in the human visual system. Deep neural networks can perform like humans in this task. However, we do not know how object recognition under more challenging conditions like occlusion is solved. Some computational models imply that recurrent processing might be a solution to the beyond core object recognition task. The other potential mechanism for solving occlusion is to reconstruct the occluded part of the object taking advantage of generative models. Here we used Conditional Generative Adversarial Networks for reconstruction. For reasonable size occlusion, we were able to remove the effect of occlusion and we recovered the performance of the base model. We showed getting the benefit of GANs for reconstruction and adding information by generative models can cause a better performance in the object recognition task under occlusion.