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Joint hierarchical learning for efficient multi-class object detection

Abstract : In addition to multi-class classification, the multi-class object detection task consists further in classifying a dominating background label. In this work, we present a novel approach where relevant classes are ranked higher and background labels are rejected. To this end, we arrange the classes into a tree structure where the classifiers are trained in a joint framework combining ranking and classification constraints. Our convex problem formulation naturally allows to apply a tree traversal algorithm that searches for the best class label and progressively rejects background labels. We evaluate our approach on the PASCAL VOC 2007 dataset and show a considerable speed-up of the detection time with increased detection performance.
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Submitted on : Thursday, March 31, 2016 - 5:11:24 PM
Last modification on : Saturday, June 25, 2022 - 9:10:02 PM


  • HAL Id : hal-01296239, version 1



Hamidreza Odabai Fard, Mohamed Chaouch, Quoc-Cuong Pham, Antoine Vacavant, Thierry Chateau. Joint hierarchical learning for efficient multi-class object detection. Applications of Computer Vision (WACV), 2014 IEEE Winter Conference on, Mar 2014, Steamboat Springs, United States. pp.261--268. ⟨hal-01296239⟩



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