[Seminar] On the Challenges of Modelling Complex Human Behaviors from Visual Information
Friday, February 26, 2021
11:00 am - 12:00 pm
Online via MS Teams
Dr. Hugo Escalante
Looking at People (LaP) is the field of computer vision dealing with the analysis of human behavior from visual information. Great improvements have been reported in this field for the so-called “obviously visual” behaviors (e.g., gesture recognition, pose estimation, etc.). However, it is only recently that the community is targeting more complex human behaviours that are not visually evident and therefore require additional information and specialized mechanisms for their analysis. In this talk I will describe past and ongoing work on the automated analysis of such subconscious human behaviors by using multimodal information. Focusing on the inherent difficulties of these tasks, resources, and open problems.
About the Speaker:
Dr. Hugo Escalante is a senior research scientist at INAOE, Mexico and secretary and member of the board of directors of Cha Learn USA, Chair officer of the IAPR Technical Committee 12. He is a member of the Mexican Academy of Sciences (AMC), the Mexican Academy of Computing (AMEXCOMP) and Mexican System of Researchers Level II (SNI). Since 2017, he is editor of the Springer Series on Challenges in Machine Learning. He has been involved in the organization of several challenges in machine learning and computer vision collocated with top venues, see http://chalearnlap.cvc.uab.es/. He has served as co-editor of special issues in IJCV, IEEE TPAMI, and IEEE Transactions on Affective Computing. He has served as competition chair of NeurIPS2020, FG2020 and ICPR2020, NeurIPS2019, PAKDD2019-2018, IJCNN2019. His research interests are on machine learning, challenge organization, and its applications on language and vision.
- Online via MS Teams