Faculty Candidate Seminar|
Compositional Visual Intelligence
Monday, March 05, 2018|
10:30am - 11:30am
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About the Event
The field of computer vision has made enormous progress in the last few years, largely due to convolutional neural networks. Despite success on traditional computer vision tasks, our systems are still a long way from the general visual intelligence of people. I will argue that an important facet of visual intelligence is composition - understanding of the whole derives from an understanding of the parts. To achieve the goal of compositional visual intelligence, we must explore new computer vision tasks, create new datasets, and develop new models that exploit compositionality. I will discuss the Visual Genome dataset which we created in service of these goals, and three research directions enabled by this new data where incorporating compositionality results in systems with richer visual intelligence.
Justin is a PhD candidate in Computer Science at Stanford University, advised by Fei-Fei Li. His research interests lie at the intersection of computer vision and machine learning. Since 2015 he has co-taught a Stanford course on convolutional neural networks and deep learning with Andrej Karpathy, Serena Yeung, and Fei-Fei Li which has been viewed hundreds of thousands of times online. He received his BS in Mathematics and Computer Science at the California Institute of Technology, and during his PhD he has spent time at Google Cloud AI, Facebook AI Research, and Yahoo Research.
Open to: Public