Adaptive region pooling for object detection

Yi-Hsuan Tsai, Onur C. Hamsici, Ming-Hsuan Yang
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
more » ... sion model is learned with samples selected by a coarse region matching scheme. The proposed algorithm performs favorably on the PASCAL VOC 2007 dataset against existing algorithms. We demonstrate the benefits of our feature pooling method when compared to conventional spatial pyramid pooling features. We also show that object information can be transferred through exemplars for detected objects.
doi:10.1109/cvpr.2015.7298673 dblp:conf/cvpr/TsaiHY15 fatcat:p65xbf3mxfaxdnjgyqsl5dqjte