TY - GEN
T1 - Adaptive region pooling for object detection
AU - Tsai, Yi Hsuan
AU - Hamsici, Onur C.
AU - Yang, Ming Hsuan
N1 - Publisher Copyright:
© 2015 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2015/10/14
Y1 - 2015/10/14
N2 - 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 regression 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.
AB - 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 regression 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.
UR - http://www.scopus.com/inward/record.url?scp=84959208354&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84959208354&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2015.7298673
DO - 10.1109/CVPR.2015.7298673
M3 - Conference contribution
AN - SCOPUS:84959208354
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 731
EP - 739
BT - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
PB - IEEE Computer Society
T2 - IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Y2 - 7 June 2015 through 12 June 2015
ER -