scholarly journals A Hybrid RNN-HMM Approach for Weakly Supervised Temporal Action Segmentation

2020 ◽  
Vol 42 (4) ◽  
pp. 765-779 ◽  
Author(s):  
Hilde Kuehne ◽  
Alexander Richard ◽  
Juergen Gall
Keyword(s):  
Action Segmentation ◽  
Weakly Supervised ◽  
Temporal Action

Multi-Hierarchical Category Supervision for Weakly-Supervised Temporal Action Localization

2021 ◽  
pp. 1-1
Author(s):  
Guozhang Li ◽  
Jie Li ◽  
Nannan Wang ◽  
Xinpeng Ding ◽  
Zhifeng Li ◽  
...  
Keyword(s):  
Action Localization ◽  
Weakly Supervised ◽  
Temporal Action

Multi-Scale Structure-Aware Network for Weakly Supervised Temporal Action Detection

2021 ◽  
pp. 1-1
Author(s):  
Wenfei Yang ◽  
Tianzhu Zhang ◽  
Zhendong Mao ◽  
Yongdong Zhanga ◽  
Qi Tian ◽  
...  
Keyword(s):  
Scale Structure ◽  
Action Detection ◽  
Multi Scale ◽  
Weakly Supervised ◽  
Temporal Action

scholarly journals Relational Prototypical Network for Weakly Supervised Temporal Action Localization

2020 ◽  
Vol 34 (07) ◽  
pp. 11053-11060
Author(s):  
Linjiang Huang ◽  
Yan Huang ◽  
Wanli Ouyang ◽  
Liang Wang
Keyword(s):  
State Of The Art ◽  
Previous Method ◽  
The State ◽  
Localization Method ◽  
Action Localization ◽  
Art Methods ◽  
Prototype Learning ◽  
Weakly Supervised ◽  
Temporal Action

In this paper, we propose a weakly supervised temporal action localization method on untrimmed videos based on prototypical networks. We observe two challenges posed by weakly supervision, namely action-background separation and action relation construction. Unlike the previous method, we propose to achieve action-background separation only by the original videos. To achieve this, a clustering loss is adopted to separate actions from backgrounds and learn intra-compact features, which helps in detecting complete action instances. Besides, a similarity weighting module is devised to further separate actions from backgrounds. To effectively identify actions, we propose to construct relations among actions for prototype learning. A GCN-based prototype embedding module is introduced to generate relational prototypes. Experiments on THUMOS14 and ActivityNet1.2 datasets show that our method outperforms the state-of-the-art methods.

scholarly journals Weakly Supervised Temporal Action Localization with Segment-Level Labels

2021 ◽  
pp. 42-54
Author(s):  
Xinpeng Ding ◽  
Nannan Wang ◽  
Jie Li ◽  
Xinbo Gao
Keyword(s):  
Action Localization ◽  
Weakly Supervised ◽  
Temporal Action
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