Multi-task Clustering of Human Actions by Sharing Information

Xiaoqiang Yan, Shizhe Hu, Yangdong Ye
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
more » ... ance of individual task. Our motivation is that, different action collections always share many similar action patterns, and exploiting the shared information can lead to improved performance. Specifically, MTIB generally formulates this problem as an information loss minimization function. In this function, the shared information can be quantified by the distributional correlation of clusters in different tasks, which is based on a high-level common vocabulary constructed through a novel agglomerative information maximization method.
doi:10.1109/cvpr.2017.431 dblp:conf/cvpr/YanHY17 fatcat:sp6yaej7w5bsxprun6n2al2zoq