TY - GEN
T1 - SFCM
T2 - 2018 IEEE International Conference on Multimedia and Expo, ICME 2018
AU - Li, Zongxian
AU - Shi, Yemin
AU - Tian, Yonghong
AU - Zeng, Wei
AU - Wang, Yaowei
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - The weakly supervised object localization (WSOL) is to locate the objects in an image while only image-level labels are available during the training procedure. In this work, the Selective Feature Category Mapping (SFCM) method is proposed, which introduces the Feature Category Mapping (FCM) and the widely-used selective search method to solve the WSOL task. Our FCM replaces layers after the specific layer in the state-of-the-art CNNs with a set of kernels and learns the weighted pooling for previous feature maps. It is trained with only image-level labels and then map the feature maps to their corresponding categories in the test phase. Together with selective search method, the location of each object is finally obtained. Extensive experimental evaluation on ILSVRC2012 and PASCAL VOC2007 benchmarks shows that SFCM is simple but very effective, and it is able to achieve outstanding classification performance and outperform the state-of-the-art methods in the WSOL task.
AB - The weakly supervised object localization (WSOL) is to locate the objects in an image while only image-level labels are available during the training procedure. In this work, the Selective Feature Category Mapping (SFCM) method is proposed, which introduces the Feature Category Mapping (FCM) and the widely-used selective search method to solve the WSOL task. Our FCM replaces layers after the specific layer in the state-of-the-art CNNs with a set of kernels and learns the weighted pooling for previous feature maps. It is trained with only image-level labels and then map the feature maps to their corresponding categories in the test phase. Together with selective search method, the location of each object is finally obtained. Extensive experimental evaluation on ILSVRC2012 and PASCAL VOC2007 benchmarks shows that SFCM is simple but very effective, and it is able to achieve outstanding classification performance and outperform the state-of-the-art methods in the WSOL task.
KW - Global Learnable Pooling (GLP)
KW - Selective Feature Category Mapping (SFCM)
KW - Weakly Supervised Object Localization (WSOL)
UR - https://www.scopus.com/pages/publications/85061449498
U2 - 10.1109/ICME.2018.8486593
DO - 10.1109/ICME.2018.8486593
M3 - 会议稿件
AN - SCOPUS:85061449498
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2018 IEEE International Conference on Multimedia and Expo, ICME 2018
PB - IEEE Computer Society
Y2 - 23 July 2018 through 27 July 2018
ER -