In the last decade, many areas of computer vision have progressed to a level supporting reliable, and sometimes impressive, applications. I wil talk about two such domains, fine-grained recognition and visual retrieval. In the fine-grained recognition, I'll discuss the issue of prior probability shift, classifier calibration and the choice of loss functions driven by applications which are often not well aligned with what is common in the research community. The problem of image retrieval will be mentioned in the context of applications where both geometric reasoning and machine learing models are needed. I'll also touch on the issue of out-of-distribution datas.
Jiri Matas is a full professor at the Center for Machine Perception, Czech Technical University in Prague. He holds a PhD degree from the University of Surrey, UK (1995). He has published more than 250 papers in refereed journals and conferences. His publications have about 60000 citations registered in Google Scholar; his h-index is 92.
He received the best paper prize e.g. at the British Machine Vision Conferences in 2002 and 2005, at the Asian Conference on Computer Vision in 2007 and at Int. Conf. on Document analysis and Recognition in 2015. J. Matas has served in various roles at major international computer vision conferences (e.g. ICCV, CVPR, ICPR, NIPS, ECCV), co-chairing ECCV 2004, 2016, 2022 and CVPR 2007 and 2022. He is an Editor-in-Chief of IJCV and was an Associate Editor-in-Chief of IEEE T. PAMI. He served on the computer science panel of ERC.
His research interests include visual tracking, object recognition, image matching and retrieval, sequential pattern recognition, and RANSAC- type optimization metods. He has co-founded two companies, Eyedea Recognition (computer vision) and Locksley (combinatorial optimization).