|COOKIES: By using this website you agree that we can place Google Analytics Cookies on your device for performance monitoring.|
Strategies for General Recognition
If you have a question about this talk, please contact Microsoft Research Cambridge Talks Admins.
This event may be recorded and made available internally or externally via http://research.microsoft.com. Microsoft will own the copyright of any recordings made. If you do not wish to have your image/voice recorded please consider this before attending
One of the major challenges in computer vision is general recognition, the ability to infer useful properties of objects within sight. It’s a slippery problem whose solution depends on the details of a particular situation—what do I want to know, what kind of object, how similar to those I have seen before. I’ll present the work from my group over the past few years on how to organize and infer knowledge of objects, with a focus on generalizing to new types of objects, providing details, and learning models of parts. I will highlight some lessons learned and also point to problems uncovered.
This talk is part of the Microsoft Research Machine Learning and Perception Seminars series.
This talk is included in these lists:
Note that ex-directory lists are not shown.
Other listsCUiD - Cambridge University International Development Society Cellular Medicine Seminar Series Early Modern Economic and Social History Seminars
Other talksTopfitter A note on the F-measure for evaluating record linkage algorithms (and classification methods and information retrieval systems) Innovation Forum 2016 - Leaders Conference Dr David Bending: A novel tool to visualize and manipulate the dynamics of T cell regulation in vivo Statistical Issues and Reliability of Eyewitness Identification Whipple Museum of the History of Science - Private tour