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Multi-task Clustering of Human Actions by Sharing Information
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Sharing information between multiple tasks can enhance the accuracy of human action recognition systems. However, using shared information to improve multi-task human action clustering has never been considered before, and cannot be achieved using existing clustering methods. In this work, we present a novel and effective Multi-Task Information Bottleneck (MTIB) clustering method, which is capable of exploring the shared information between multiple action clustering tasks to improve the
doi:10.1109/cvpr.2017.431
dblp:conf/cvpr/YanHY17
fatcat:sp6yaej7w5bsxprun6n2al2zoq