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Learning to detect partially labeled people
Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)
Deployed vision systems often encounter image variations poorly represented in their training data. While observing their environment, such vision systems obtain unlabeled data that could be used to compensate for incomplete training. In order to exploit these relatively cheap and abundant unlabeled data we present a family of algorithms called λMEEM. Using these algorithms, we train an appearance-based people detection model. In contrast to approaches that rely on a large number of manually
doi:10.1109/iros.2003.1248862
dblp:conf/iros/RachlinDK03
fatcat:vfn5tngh2zfhnbjhe7mu4qiz5i