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The phenomenal growth of video on the web and the increasing sparseness of meta information associated with it forces one to look for signals from the video content for search/information retrieval and browsing based corpus exploration. A large chunk of users' searching/browsing patterns are centered around people present in the video. Doing it at scale in videos remains hard due to the absence of labeled data for such a large set of people and the large variation of pose/illumination/expression/age/occlusion/quality, etc., in the target corpus. The authors propose a system that can learn and recognize faces by combining signals from large scale weakly labeled text, image, and video corpora.
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