Keynote at CVPR Workshop on Causality in Vision, June, 2021.
Perceptual Causality: Learning and Inferring Causality from Visual Observations and Interactions
Keynote at China3D Vision Symposium, June, 2021.
Task-oriented 3D Scene Understanding
Keynote at CICAI CAAI International Conference on AI, June, 2021.
Explainable AI: How Machines Gain Justified Human Trust
Keynote at ACL Asia, December, 2020
Explainable AI: How Machines Gain Justified Human Trust
Keynote at Beijing Academy of AI, October, 2019.
Towards General AI: From "Big Data" to "Big Task"
Keynote at World AI Conference, Shanghai, August, 2019
Dark, Beyond Deep: a Paradigm Shift in AI Research
Keynote at CVPR Workshop on Explainable AI, Long Beach, CA, June 2019
Explainable AI: How Machines Gain Justified Trust from Humans
Openning Speech at CVPR Workshop on 3D Scene Understanding, Long Beach ,CA, June 2019
Some Thoughts and Principles for 3D Scene Understanding
Keynote at CVPR Workshop on vision Meets Cognition, Long Beach ,CA, June 2019
VR Platforms for Training and Evaluating Autonomous Agents: Small-Data for Big-Tasks
Keynote at ACM TURC Conference, Chengdu, May, 2019
Artificial Intelligence: The Era of Big Integration
Keynote at Tsinghua AI Summit, Sanya Math Forum, March, 2019
Explainable AI: How Machines Gain Justified Trust from Humans
Lecture at the Symposium Honoring David Mumford, CMSA, Harvard University, August, 2018
Artificial Intelligence: The Era of Big Integration
Lecture at the Rama Chellapa 65 Birthday Symposium, June, 2018
Building a "telescope" to Look Into Very High-Dimensional Image Universe
Distinguished Lecture at Hongkong PolyU, June, 2018
Artificial Intelligence: The Era of Big Integration
Keynote at Int'l Conf. on Image Processing, Beijing, September, 2017
A Tale of Three Probabilistic Families: Descriptive, Generative and Discriminative Models [ppt slides]
Symposium at NLPR, Beijing,September, 2017
Dark, Beyond Deep: A Paradigm Shift for Computer Vision
Workshop on Human-Machine Interaction, Beijing,September, 2017
A Conitive Architecture for Human-Machine Teaming.
Keynote at Int'l Workshop on Vision Meets Cognition, at CVPR, Hawaii, June, 2017.
Dark, Beyond Deep
Invited Talk at the Int'l Workshop on Vision and Language, at CVPR, Hawaii, June, 2017.
Vision + Language: Why and How
Invited Talk at Microsoft Faculty Summit, Seattle, June 2017.
Dark, Beyond Deep
Invited Talk at Hongkong Chinese University, June, 2017.
Dark, Beyond Deep
Invited Talk at SNAP Inc. April, 2017.
Advanced Topics in Vision and AI Research
Special Workshop for Zhu and associates, Microsoft Research Asia, September, 2016.
A Cognitive Architecture for Human-Robot Teaming
Computer Science Coloquium, Princeton University, Nov. 2015.
A Cognitive Architecture for Human-Robot Collaborations based on Spatial, Temporal and Causal And-Or Graph.
Keynote at the AI symposium on Human-Robot Interactions, Nov. 2015.
A Cognitive Architecture for Human-Robot Collaborations based on Spatial, Temporal and Causal And-Or Graph.
CVPR workshop on Vision Meets Cognition: Function, Physics, Intent and Causality, June 2015.
Rethink Vision from the Perspective of an Agent: Task-centered Representation, inference and learning. [ppt slides]
CVPR workshop on Language and Vision, June. 2015.
Joint Video-Text Parsing for Understanding Scenes and events and Query Answering [ppt slides]
CogSci Workshop on Physical and Social Scene Understanding, July 2015.
Understanding Scenes and Events by Reasoning Physics and Causality.
Distinguished Lecture series, Dept. of Computer Sceience and Engineering, UCSD, Nov. 2014.
Scene Understanding by Reasoning Functionality, Physics, Intents and Causality.
National Cheng Kung University, Taiwan, September 2014.
Understanding Scenes and Events by Joint Spatial, Temporal, and Causal Inference
Academia Sinica, Taiwan, September, 2014
Lecture 1: Pursuing a Unified Statistical Model for Regimes of Image Patterns.
Lecture 2: Understanding Scenes and Events by Joint Spatial, Temporal, and Causal Inference
National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Science, July 2014
Beyond What and Where: Reasoning Function, Physics, Intent and Causality [ppt_slides]
Institute of AI and Robotics, Xi'an Jiaotong University, July 2014
Beyond What and Where: Reasoning Function, Physics, Intent and Causality
CVPR workshop on Vision Meets Cognition: Function, Physics, Intent and Causality, June 2014
Beyond What and Where: Spatial, Temporal and Causal Parsing with Commonsense Knowledge
Stanford Workshop on AI and Knowledge, April, 2014.
Understanding Video and Text by Joint Spatial, Temporal, and Causal Inference [ppt_slides]
UC Berkeley Vision Seminar, April, 2014.
Beyond What and Where: Joint Video-Text Inference with Commonsense Reasoning
Computer Science and Engineering Department Distinguished Speaker series, SUNY Buffalo, Feb. 2014.
Understanding Video and Text by Joint Spatial, Temporal, and Causal Inference
Center for Image Science, Johns Hopkins University, Feb. 2014.
Pursuing a Unified Representsation and Model of Visual Knowledge
Scene Understanding workshop at CVPR, June 2013
Scene Understanding by Inferring the "Dark Matters": Physics, Funcationality, Causality and Minds [ppt_slides]
Third Conference of Tsinghua Sanya International Mathematics Forum, Jan, 2013
Stochastic Sets and Regimes of Mathematical Models of Images [ppt slides]
Seminar at Peking University, August, 2012
Understanding images and Video by joint Spatial, Temporal and Causal Inference
Beijing Int'l Summer School on Vision, Cognition and Learning, August, 2012
Lecture 1: Object and scene representation and parsing
Lecture 2: Event parsing and inferring agent��s intents and goals
Lecture 3: Open problems and challenges in vision, learning and cognition
Tutorial at the Stochastic Image Grammar (SiG), June, 2012
Lecture 1: Spatial AND-OR graph for representing the scene-object-part-primitive hierarchy
Lecture 2: Causal AND-OR graphs (C-AOG) for representing the causal-action recursion for reasoning
Lecture 3: Information projection for learning S-AOG, T-AOG, C-AOG
UC Riverside EE Colloqium, January, 2012
Learning Visual Knowledge by Information Projection [ppt_slides]
UC LA CS seminar, May, 2012
Spatial, temporal, and Causal Inference for Understabding Image and Video
NSF Workshop on Frontier of Vision, August, 2011.
Lecture 1:Hack, Math, and Stat [ppt slides]
Lecture 2:Visual Conceptualization by Stochastic Sets [ppt slides]
IPAM Summer School on Probabilistic Models of Cognition, July, 2011.
Lecture 1: Learning And-Or Graph Representations for Objects and Events
Lecture 2: Top-down/Bottom-up Inference in And-Or graphs
Microsoft Research Asia, August 2010
The Joy of Ambiguities in Vision and Visual Arts [ppt_slides]
Seminar at the Redwood Center for Theoretical Neuroscience at UC Berkeley on November 12, 2009
Information Scaling and Perceptual Transitions in Natural Images and Video [ppt_slides]
Beijing Summer School on Vision, Learning, and Pattern Recognition, July 2009,
Lecture 1: Pursuing Manifolds in the Universe of Images [pdf]
Lecture 2: Inference in And-Or Graphs [pdf]
The J.K. Aggarwal Lecture, a plenary speech at the ICPR08, Dec.10, 2008,
Pursuing Implicit and Explicit Manifolds by Information projection [pdf]
Plenary Speech at the Chinese Conference on Pattern Recognition, Oct. Beijing,
Psychology Workshop on Natural Environments Tasks and Intelligence, March, 2008, UT Austin,
Information Scaling and Manifolds Learning in Natural Images and Video
Computer Vision Distinguished Speaker Series, Univ. of Central Florida, March, 2008,
Visual Learning and Conceptualization: pursuing manifolds in the universe of images.
NIPS workshop on the Grammar of Vision, Dec. 2007, Van Couver, Canada,
Object Category by Stochastic Grammar
Object Video Seminar, Nov. 2007,
Object Recognition in Natural and Aerial Images
Int'l Workshop on Object Categorization, Oct. 2007, Rio, Brazil.
Object Category Modeling, Learning, and Recognition by Stochastic Grammar [pdf]
MIT Vision Symposium, May, 2007, Cambridge, MA.
Object Category Modeling, Learning, and Recognition by Stochastic Grammar.
Symposium on the Mathematics of Perception, June, 2007. Newport, RI.
Quest for a Stochastic Grammar of Images.
Chinese Academy of Science, Institute of Computing Seminar, Mar. 2007, Beijing.
Computer Vision and High Performance Computing.
ChangJiang Scholar Lectureship, Huazhong University of Science and Technology, Mar. 2007,
Foundation and Trend in Computer Vision.
Workshop on Texture and Natural Image Processing, Jan. 2007, Paris, France.
Texture, Texton and Primal Sketch: Integrating MRF and Wavelets
International Symposium on Vision by Brains and Machines, Nov. 2006, MonteVideo, Uruguay.
Visual Learning with Implicit and Explicit Manifolds [pdf]
Dragon Star Lecture Series, July 2006
Modeling, Learning, and Conceptualizing Visual Patterns.
Int'l Workshop on Semantic Learning, June 2006,
Knowledge Representation and Learning Schemes for Large Object Categories
IMA workshop on Visual Learning and Recognition, May 2006
Advocate for Generative Models [pdf]
Neyman seminar, Berkeley Statistics Department, April 2005,
Cluster Sampling and Data-Driven Markov Chain Monte Carlo [pdf]
Math Science Research Institute, Berkeley, Workshop on Visual Recognition, Mar. 2005,
Context Sensitive Graph Grammar and Top-down/Bottom-up Inference [pdf]
Math Science Research Institute, Berkeley, Workshop on Low-Middle level Vision, Feb. 2005,
From Primal Sketch to 2 1/2 D Sketch [pdf]
Math Science Research Institute, Berkeley, Workshop on Introduction to Vision, Jan. 2005,
Seeing as Statistical Inference [pdf]
IPAM workshop on Multiscale Geometric Analysis, 2004
From Scaling Laws of Natural Images to Regimes of Image Models [pdf]
Technical Univ. of Denmark, 2003
Inti'l Workshop on Object Recognition, 2003
Visual Inference by Markov Chain Monte Carlo Methods [pdf]
University of South California, 2003
A talk to the Psychology Dept. at UCLA, A Math Theory for Texture, Texton, Primal Sketch and Gestalt Fields [pdf]
Interface meeting 2002
Kodak research lab., 2001.
Microsoft Research Beijing, 2001.
Univ. of California, Los Angeles, 2001.
The Abdus Salum International Centre for Theoretical Physics, Italy, (Teaching Short Courses), 2000.
Microsoft Research, Beijing, 2000.
Pattern Theory Seminar, Brown University, 2000.
Brown University, 1999.
University of Chicago, 1999.
Institute of Henri Poincare, Paris, France. 1998.
Inria at Antipolis, FRANCE. 1998.