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Humans, even young infants, are adept at perceiving and understanding complex indoor scenes. Such an incredible vision system relies on not only the data-driven pattern recognition but also roots from the visual reasoning system, known as the core knowledge, that facilitates the 3D holistic scene understanding tasks. This talk discusses how to employ physical common sense and human-object interaction to bridge scene and human understanding and how the part-level 3D affordance perception may lead to a more fine-grained human-object interaction modeling. Future directions may be extended to dynamic scene understanding and understanding the human communications in the scene.
Yixin Chen (University of California, Los Angeles (UCLA))
PhD student
Yixin Chen is a PhD student advised by Prof. Song-Chun Zhu in Center for Vision, Cognition, Learning, and Autonomy (VCLA) in the Department of Statistics at University of California, Los Angeles (UCLA). Previously, Yixin received a M.S. in Electrical and Computer Engineering at UCLA. Yixin's research interests focus on computer vision and cognition, particularly in the areas of scene understanding, human-object interaction and social cue understanding. Yixin's goal is to help machines reach human-like understanding from visual input.