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Adaptive region pooling for object detection
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning models for object detection is a challenging problem due to the large intra-class variability of objects in appearance, viewpoints, and rigidity. We address this variability by a novel feature pooling method that is adaptive to segmented regions. The proposed detection algorithm automatically discovers a diverse set of exemplars and their distinctive parts which are used to encode the region structure by the proposed feature pooling method. Based on each exemplar and its parts, a
doi:10.1109/cvpr.2015.7298673
dblp:conf/cvpr/TsaiHY15
fatcat:p65xbf3mxfaxdnjgyqsl5dqjte