Arnold Smeulders, Laurens van der Maaten and myself are organizing a new Ph.D. course on Computer Vision by Learning. The first edition will happen from March 25 to March 31, in Amsterdam. This ASCI course is especially meant for Ph.D. students who have basic familiarity with computer vision, image processing, and pattern recognition and want to upsurge their knowledge and machinery to the state-of-the-art, with direct utility in their own research. The topic of attention is the challenges of computer vision by learning. We address the theoretical foundations of machine learning in conjunction with computer vision and present algorithms that achieve state-of-the-art performance while maintaining efficient execution with minimal supervision. We explain and emphasize machine learning for vision tasks like concept detection with deep learning, fine-grained categorization using kernel pooling, semantic segmentation with conditional random fields, object tracking by structured SVMs, event recognition by random forests and retrieval from a single image by metric learning. We give an overview of the latest developments and future trends in the field on the basis of several recent challenges, including the TRECVID and ImageNet competitions, the leading competitions for visual search engines based on computer vision by learning, and we indicate how to obtain improvements in the near future. The course will close with an invited tutorial by the renown prof. Shih-Fu Chang from Columbia University, USA.
