NIPS2009_0142_slide - Evaluating multi-class learning...

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Unformatted text preview: Evaluating multi-class learning strategies in a hierarchical framework for object detection. Sanja Fidler, Marko Boben, Aleš Leonardis. Summary Objects are represented with a hierarchical compositional shape vocabulary which is learned from images. We evaluate 3 different strategies for learning a hierarchical multi-class object vocabulary for object detection: independent, joint and sequential training. We explore and compare their computational behavior (space and time) and detection performance as a function of the number of learned object classes on several recognition datasets. Results 10 classes. 50 classes. Detections Sharing between visually similar and dissimilar classes. 2 classes. Ind. Joint Seq. Joint ...
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This note was uploaded on 02/12/2010 for the course COMPUTER S 10586 taught by Professor Jilinwang during the Fall '09 term at Zhejiang University.

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