scholarly journals Weakly Supervised Action Detection

2011 ◽  
Author(s):  
Parthipan Siva ◽  
Tao Xiang
Keyword(s):  
Action Detection ◽  
Weakly Supervised

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 WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

2021 ◽  
Author(s):  
Mingfei Gao ◽  
Yingbo Zhou ◽  
Ran Xu ◽  
Richard Socher ◽  
Caiming Xiong
Keyword(s):  
Action Detection ◽  
Weakly Supervised

scholarly journals Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos

2020 ◽  
pp. 751-768
Author(s):  
Anurag Arnab ◽  
Chen Sun ◽  
Arsha Nagrani ◽  
Cordelia Schmid
Keyword(s):  
Action Detection ◽  
Weakly Supervised

Weakly Supervised Temporal Action Detection with Temporal Dependency Learning

2021 ◽  
pp. 1-1
Author(s):  
Bairong Li ◽  
Ruixin Liu ◽  
Tianquan Chen ◽  
Yuesheng Zhu
Keyword(s):  
Action Detection ◽  
Weakly Supervised ◽  
Temporal Action

scholarly journals Segregated Temporal Assembly Recurrent Networks for Weakly Supervised Multiple Action Detection

2019 ◽  
Vol 33 ◽  
pp. 9070-9078 ◽  
Author(s):  
Yunlu Xu ◽  
Chengwei Zhang ◽  
Zhanzhan Cheng ◽  
Jianwen Xie ◽  
Yi Niu ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Star Network ◽  
Temporal Gradient ◽  
Action Detection ◽  
Video Clips ◽  
Weakly Supervised ◽  
Class Labels ◽  
Class Variable ◽  
Activation Mapping

This paper proposes a segregated temporal assembly recurrent (STAR) network for weakly-supervised multiple action detection. The model learns from untrimmed videos with only supervision of video-level labels and makes prediction of intervals of multiple actions. Specifically, we first assemble video clips according to class labels by an attention mechanism that learns class-variable attention weights and thus helps the noise relieving from background or other actions. Secondly, we build temporal relationship between actions by feeding the assembled features into an enhanced recurrent neural network. Finally, we transform the output of recurrent neural network into the corresponding action distribution. In order to generate more precise temporal proposals, we design a score term called segregated temporal gradient-weighted class activation mapping (ST-GradCAM) fused with attention weights. Experiments on THUMOS’14 and ActivityNet1.3 datasets show that our approach outperforms the state-of-theart weakly-supervised method, and performs at par with the fully-supervised counterparts.

scholarly journals Weakly Supervised Gaussian Networks for Action Detection

2020 ◽  
Author(s):  
Basura Fernando ◽  
Cheston Tan Yin Chet ◽  
Hakan Bilen
Keyword(s):  
Action Detection ◽  
Gaussian Networks ◽  
Weakly Supervised

Transferable Knowledge-Based Multi-Granularity Fusion Network for Weakly Supervised Temporal Action Detection

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Haisheng Su ◽  
Xu Zhao ◽  
Tianwei Lin ◽  
Shuming Liu ◽  
Zhilan Hu
Keyword(s):  
Action Detection ◽  
Knowledge Based ◽  
Weakly Supervised ◽  
Temporal Action

Weakly Supervised Temporal Action Detection with Shot-Based Temporal Pooling Network

2018 ◽  
pp. 426-436 ◽  
Author(s):  
Haisheng Su ◽  
Xu Zhao ◽  
Tianwei Lin ◽  
Haiping Fei
Keyword(s):  
Action Detection ◽  
Weakly Supervised ◽  
Temporal Action

Mask attention-guided graph convolution layer for weakly supervised temporal action detection

2021 ◽  
Author(s):  
Mengyao Zhao ◽  
Zhengping Hu ◽  
Shufang Li ◽  
Shuai Bi ◽  
Zhe Sun
Keyword(s):  
Action Detection ◽  
Weakly Supervised ◽  
Temporal Action
Sign in / Sign up

Export Citation Format

Share Document