Learning to Count with Back-propagated Information

Error back-propagation is one of the principled learning strategies widely used in pattern recognition and machine learning, e.g. neural networks. The existing frameworks employed back-propagated error as a performance criteria (or termed, object function) aiming for supervising model-learning. Inspired by the recent success achieved by learning with the privileged information (LPI), we propose a novel regression-based framework by extending the concept of back-propagation in supervised learning methods to high-level guiding the model learning, so the proposed model is able to mine the importance of samples contributed to the fitting performance, which is missed in the existing regression techniques. To verify the effectiveness of the proposed learning paradigm, both low-level imagery features and intermediary semantic attributes are adopted in this paper. Extensive evaluations on pedestrian counting with public UCSD and Mall benchmarks demonstrate that the effectiveness of the proposed framework.