Carnegie Mellon University

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VASC Seminar: Xinlei Chen
NEIL: Extracting Visual Knowledge from Web Data

Xinlei Chen
PhD Student, Language Technologies Institute, Carnegie Mellon University

November 25, 2013, 3:00-4:30PM, NSH 1507

NEIL (Never Ending Image Learner) is a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. NEIL uses a semi-supervised learning algorithm that jointly discovers common sense relationships (e.g., “Corolla is a kind of/looks similar to Car”,“Wheel is a part of Car”) and labels instances of the given visual categories. It is an attempt to develop the world’s largest visual structured knowledge base with minimum human labeling effort. As of 10th October 2013, NEIL has been continuously running for 2.5 months on 200 core cluster (more than 350K CPU hours) and has an ontology of 1152 object categories, 1034 scene categories and 87 attributes. During this period, NEIL has discovered more than 1700 relationships and has labeled more than 400K visual instances.

Additional Information

Host: Kris Kitani

Speaker Biography

Xinlei Chen is a PhD student in the Language Technologies Institute at Carnegie Mellon University, where he is supervised by Abhinav Gupta. He holds an Bachelor's degree in Computer Science from Zhejiang University, China. His research focuses on the intersection of computer vision and natural language processing and he is particularly interested in data-driven algorithms for life-long learning.