To develop machines that have the following capabilities.

Achieve deep understanding of scenes and events through joint parsing and cognitive reasoning about appearance, geometry, functions, physics, causality, intents and belief of agents, and use joint and long-ranged reasoning to fill the performance gap with human vision.

Represent visual knowledge in probabilistic compositional models across the spatial, temporal, and causal hierarchies augmented with rich relations, which are task-oriented, support efficient task-dependent inference from an agent’s perspective, and preserve uncertainties.

Acquire massive visual commonsense through web scale continuous lifelong learning from heterogeneous sources through weakly supervised HCI and dialogue with humans.

Understand human needs and values to interact with humans effectively and answer human queries about what, who, where, when, why and how in storylines through Turing tests.


Principal Investigator

  • Song-Chun Zhu
    Professor of Statistics and Computer Science

    Director of the UCLA Center for Vision, Cognition, Learning and Autonomy

    Research Interest:
    Computer Vision, Statistical Learning, Cognition, AI, Robot Autonomy

Members in the US

  • Martial Hebert
    Professor of Robotics
    Carnegie Mellon University

    Director of the CMU Robotics Institute

    Research Interest:
    Computer Vision, Robotics and AI

    Abhinav Gupta
    Assistant Professor
    Carnegie Mellon University

    Research Interest:
    Computer Vision and Lifelong learning

    Nancy Kanwisher
    Walter A. Rosenblith Professor of Cognitive Neuroscience
    Department of Brain and Cognitive Sciences

    Research Interest:
    Cognitive Neuroscience

    Josh Tenenbaum
    Professor of Computational Cognitive Science
    Department of Brain and Cognitive Sciences

    Research Interest:
    Cognitive modeling for perception, learning, reasoning in humans and machine

    Fei-Fei Li
    Associate Professor of Computer Science
    Stanford University
    Director of the Stanford Artificial Intelligence Lab

    Research Interest:
    Computer Vision, Neurosciences, and Artificial Intelligence

    David Forsyth
    Professor of Computer Science
    University of Illinois at Urbana-Champaign

    Research Interest:
    Machine Learning, Graphics

    Derek Hoiem
    Associate Professor of Computer Science
    University of Illinois at Urbana-Champaign

    Research Interest:
    General visual scene understanding

    Brian Scholl
    Professor of Psychology and Cognitive Science
    Yale University

    Chair of the Cognitive Science Program
    Director of the Yale Perception & Cognition Laboratory

    Research Interest:
    Perception of Causality, Animacy, and Intentionality

Members in the UK

  • Philip Torr
    Professor of Engineering
    University of Oxford

    Director of Torr Vision Group

    Research Interest:
    Computer Vision and Machine Learning

    Philippe Schyns
    Professor of Visual Cognition
    University of Glasgow

    Director of the Institute of Neuroscience and Psychology

    Research Interest:
    Rapid Scene Perception, Task-dependent categorization

    Andrew Glennerster
    Professor of Visual Neuroscience
    University of Reading

    Research Interest:
    3D Scene Perception in high-fidelity immersive reality

    Ales Leonardis
    Chair Professor of Robotics
    University of Birmingham

    Co-Director of Computational Neuroscience and Cognitive Robotics Centre

    Research Interest:
    Computer Vision, Compositional Modeling, and Cognitive Robotics



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Patrick Tam (patrick.tam@stat.ucla.edu): Hotel and Direction

Yixin Zhu (yixin.zhu@ucla.edu): Schedule