Multi-task Information Bottleneck Co-clustering for Unsupervised Cross-view Human Action Categorization

• Published in: ACM transactions on knowledge discovery from data Vol. 14; no. 2; pp. 1 - 23
• Main Authors: Yan, Xiaoqiang, Lou, Zhengzheng, Hu, Shizhe, Ye, Yangdong
• Format: Journal Article
• Language: English
• Published: 30.04.2020
• ISSN: 1556-4681; 1556-472X
• DOI: 10.1145/3375394

The widespread adoption of low-cost cameras generates massive amounts of videos recorded from different viewpoints every day. To cope with this vast amount of unlabeled and heterogeneous data, a new multi-task information bottleneck co-clustering (MIBC) approach is proposed to automatically categorize human actions in collections of unlabeled cross-view videos. Our motivation is that, if a learning action category from each view is seen as a single task, it is reasonable to assume that the tasks of learning action patterns from the videos recorded by multiple cameras are dependent and inter-related, since the actions of the same subjects synchronously recorded from different camera viewpoints are complementary to each other. MIBC aims to transfer the shared view knowledge across multiple tasks (i.e., camera viewpoints) to boost the performance of each task. Specifically, MIBC involves the following two parts: (1) extracting action categories for each task by independently maintaining its own relevant information, and (2) allowing the feature representations of all tasks to be compressed into a common feature space, which is utilized to capture the relatedness of multiple tasks and transfer the shared knowledge across different camera viewpoints. These two parts of MIBC work simultaneously and can be solved in a novel co-clustering mechanism. Our experimental evaluation on several cross-view action collections shows that the MIBC algorithm outperforms the existing state-of-the-art baselines.