Prof. Antonio Torralba is with the Computer Science and Artificial Intelligence Laboratory in the Dept. of Electrical Engineering and Computer Science of the Massachusetts Institute of Technology. His research is in the areas of computer vision, machine learning and human visual perception. Antonio is interested in building systems that can perceive the world like humans do. Although his work focuses on computer vision, he is also interested in other modalities such as audition and touch. A system able to perceive the world through multiple senses might be able to learn without requiring massive curated datasets. Other interests include understanding neural networks, common-sense reasoning, computational photography, building image databases, …, and the intersections between visual art and computation.
Prof. Sergio Escalera obtained the P.h.D. degree on Multi-class visual categorization systems at Computer Vision Center, UAB. He obtained the 2008 best Thesis award on Computer Science at Universitat Autònoma de Barcelona. He leads the Human Pose Recovery and Behavior Analysis Group at UB, CVC, and the Barcelona Graduate School of Mathematics. He is an associate professor at the Department of Mathematics and Informatics, Universitat de Barcelona. He is an adjunct professor at Universitat Oberta de Catalunya, Aalborg University, and Dalhousie University. He has been visiting professor at TU Delft and Aalborg Universities. He is a member of the Visual and Computational Learning consolidated research group of Catalonia. He is also a member of the Computer Vision Center at UAB. He is series editor of The Springer Series on Challenges in Machine Learning. He is Editor-in-Chief of American Journal of Intelligent Systems and editorial board member of more than 5 international journals. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is co-creator of Codalab open source platform for challenges organization. He is co-founder of PhysicalTech and Care Respite companies. He is also member of the AERFAI Spanish Association on Pattern Recognition, ACIA Catalan Association of Artificial Intelligence, INNS, and Chair of IAPR TC-12: Multimedia and visual information systems. He has different patents and registered models. He has published more than 200 research papers and participated in the organization of scientific events, including CCIA04, ICCV11, CCIA14, AMDO16, FG17, NIPS17, NIPS18, FG19, and workshops at ICCV11, ICMI13, ECCV14, CVPR15, ICCV15, CVPR16, ECCV16, ICPR16, NIPS16, CVPR17, ICCV17, NIPS17. He has been guest editor at JMLR, TPAMI, IJCV, TAC, PR, MVA, JIVP, Expert Systems, and Neural Comp. and App. He has been area chair at WACV16, NIPS16, AVSS17, FG17, ICCV17, WACV18, FG18, FG19 and competition and demo chair at FG17, NIPS17, NIPS18 and FG19. His research interests include, between others, statistical pattern recognition, affective computing, and human pose recovery and behavior understanding, including multi-modal data analysis.
Title: Apparent Human Behavior Understanding
Abstract: Automatic analysis of humans via computer vision and multi-modal approaches requires from large sets of annotated data when working in the supervised scenario. In many situations annotated data is difficult to be collected or the annotation task is influenced by factors such as the acquisition protocol or the subjectivity of the labelers/raters. In the later, we consider the annotated data to contain apparent labels. Apparent labels can be useful for training machines to perceive human attributes and behaviors in a similar way humans do. In this talk, I will present recent research and competitions organized by HuPBA group and ChaLearn Looking at people in the field of Apparent Human Behavior Understanding, including apparent age recognition and apparent personality analysis. Collected data, organized competitions, and deep learning approaches in order to deal with both apparent age and personality will be presented.
Prof. Kristen Grauman is with the Department of Computer Science at the University of Texas at Austin, where she leads the UT-Austin Computer Vision Group. She received her Ph.D. from MIT in the Computer Science and Artificial Intelligence Laboratory in 2006. Her research interests are in computer vision and machine learning, in particular, visual recognition and visual search. Recent and ongoing projects in the group consider large-scale image/video retrieval, unsupervised visual discovery, active learning, active recognition, first-person “egocentric” computer vision, interactive machine learning, image and video segmentation, activity recognition, vision and language, and video summarization.
Title: Visual styles in fashion photos