Robust object representation by boosting-like deep learning architecture

This paper presents a new deep learning architecture for robust object representation, aiming at efficiently combining the proposed synchronized multi-stage feature (SMF) and a boosting-like algorithm. The SMF structure can capture a variety of characteristics from the inputting object based on the fusion of the handcraft features and deep learned features. With the proposed boosting-like algorithm, we can obtain more convergence stability on training multi-layer network by using the boosted samples. We show the generalization of our object representation architecture by applying it to undertake various tasks, i.e. pedestrian detection and action recognition. Our approach achieves 15.89% and 3.85% reduction in the average miss rate compared with ACF and JointDeep on the largest Caltech dataset, and acquires competitive results on the MSRAction3D dataset.