Spatial, Temporal and Casual Inference for Understanding Images and Video

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Images and Video, PROF SONG-CHUN ZHU, UCLA: Statistics – CS Department CS 201: Spatial, Temporal and Casual Inference for Understanding Images and Vid

Prof Song-Chun Zhu
UCLA Jointly: Statistics – Computer Science Department

In the race of machine vs. human, computers are gaining grounds from symbolic world like playing chess, to the real world like jeopardy and autonomous driving. The last highland humans hold perhaps will be vision, thank to the vast amount of visual knowledge we have and the inference power that a large portion of our brain is committed to. But this may not last very long. In this talk, I’d like to address two problems by reviewing our research in vision, cognition, and learning:

With literally unlimited amount of images and videos, can a computer learn the spatial, temporal and causal concepts and visual knowledge in an unsupervised way?
What are the learning principle and innate capacity, which are necessary and sufficient for a computer to jump start?

I will review our progress by showing demos on

deep spatial, temporal and causal parsing for understanding objects, scene and events
reasoning human intents
generating narrative text description (and speech) about the scene and events
answering human queries

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Seminar

Date: May 10, 2012
Time: from 04:15 PM to 05:45 PM
Place: 400 Boelter Hall
Contact name: Edna Todd
Contact phone: 310 825-4033