Occlusion Robust Part-aware Object Classification through Part Attention and Redundant Features Suppression

In recent studies, object classification with deep convolutional neural networks has shown poor generalization with occluded objects due to the large variation of occlusion situations. We propose a part-aware deep learning approach for occlusion robust object classification. To demonstrate the robustness of the method to unseen occlusion, we train our network without occluded object samples in training and test it with diverse occlusion samples. Proposed method shows improved classification performance on CIFAR10, STL10, and vehicles from PASCAL3D+ datasets.