Weakly-supervised DCNN for RGB-D Object Recognition in Real-World Applications Which Lack Large-scale Annotated Training Data

A novel, weakly-supervised learning architecture which combines parametric models (a pair of Deep Convolutional Neural Networks for RGB and D modalities) with non-parametric models (Gaussian Process Classification) and is demonstrated to be highly effective in solving a novel RGBD object recognition application which lacks of human annotations.