Deep Self-Taught Learning for Weakly Supervised Object Localization

Most existing weakly supervised localization (WSL) approaches learn detectors by finding positive bounding boxes based on features learned with image-level supervision. However, those features do not contain spatial location related information and usually provide poor-quality positive samples for training a detector. To overcome this issue, we propose a deep self-taught learning approach, which makes the detector learn the object-level features reliable for acquiring tight positive samples and afterwards re-train itself based on them. Consequently, the detector progressively improves its detection ability and localizes more informative positive samples. To implement such self-taught learning, we propose a seed sample acquisition method via image-to-object transferring and dense subgraph discovery to find reliable positive samples for initializing the detector. An online supportive sample harvesting scheme is further proposed to dynamically select the most confident tight positive samples and train the detector in a mutual boosting way. To prevent the detector from being trapped in poor optima due to overfitting, we propose a new relative improvement of predicted CNN scores for guiding the self-taught learning process. Extensive experiments on PASCAL 2007 and 2012 show that our approach outperforms the state-of-the-arts, strongly validating its effectiveness.Comment: Accepted as spotlight paper by CVPR 201

Analytical Solution for the SU(2) Hedgehog Skyrmion and Static Properties of Nucleons

An analytical solution for symmetric Skyrmion was proposed for the SU(2) Skyrme model, which take the form of the hybrid form of a kink-like solution and that given by the instanton method. The static properties of nucleons was then computed within the framework of collective quantization of the Skyrme model, with a good agreement with that given by the exact numeric solution. The comparisons with the previous results as well as the experimental values are also given.Comment: 4 pages, 2 figures, submited to Phys.Lett.