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2011
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Learning to Recognize Objects in Egocentric Activities

8 years 2 months ago
Learning to Recognize Objects in Egocentric Activities
This paper addresses the problem of learning object models from egocentric video of household activities, using extremely weak supervision. For each activity sequence, we know only the names of the objects which are present within it, and have no other knowledge regarding the appearance or location of objects. The key to our approach is a robust, unsupervised bottom up segmentation method, which exploits the structure of the egocentric domain to partition each frame into hand, object, and background categories. By using Multiple Instance Learning to match object instances across sequences, we discover and localize object occurrences. Object representations are refined through transduction and object-level classifiers are trained. We demonstrate encouraging results in detecting novel object instances using models produced by weaklysupervised learning.
Alireza Fathi, Xiaofeng Ren, James Rehg
Added 31 Mar 2011
Updated 29 Apr 2011
Type Journal
Year 2011
Where CVPR
Authors Alireza Fathi, Xiaofeng Ren, James Rehg
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