scholarly journals Interaction-Aware Spatio-Temporal Pyramid Attention Networks for Action Classification

2018 ◽  
pp. 388-404 ◽  
Author(s):  
Yang Du ◽  
Chunfeng Yuan ◽  
Bing Li ◽  
Lili Zhao ◽  
Yangxi Li ◽  
...  
Keyword(s):  
Action Classification ◽  
Attention Networks ◽  
Spatio Temporal

scholarly journals Interaction-Aware Spatio-Temporal Pyramid Attention Networks for Action Classification

2021 ◽  
pp. 1-1
Author(s):  
Weiming Hu ◽  
Haowei Liu ◽  
Yang Du ◽  
Chunfeng Yuan ◽  
Bing Li ◽  
...  
Keyword(s):  
Action Classification ◽  
Attention Networks ◽  
Spatio Temporal

scholarly journals Multi-Term Attention Networks for Skeleton-Based Action Recognition

2020 ◽  
Vol 10 (15) ◽  
pp. 5326
Author(s):  
Xiaolei Diao ◽  
Xiaoqiang Li ◽  
Chen Huang
Keyword(s):  
Neural Network ◽  
Time Scales ◽  
Action Recognition ◽  
State Of The Art ◽  
Attention Networks ◽  
Weighted Fusion ◽  
Temporal Features ◽  
Benchmark Datasets ◽  
Spatio Temporal ◽  
Different Time Scales

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

scholarly journals Video Question Answering via Hierarchical Spatio-Temporal Attention Networks

2017 ◽  
Author(s):  
Zhou Zhao ◽  
Qifan Yang ◽  
Deng Cai ◽  
Xiaofei He ◽  
Yueting Zhuang
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Question Answering ◽  
Temporal Dynamics ◽  
Temporal Attention ◽  
Attention Networks ◽  
Learning Framework ◽  
Spatio Temporal ◽  
The Given ◽  
Video Question Answering

Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoder-decoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the encoder-decoder learning method with reasoning recurrent neural networks for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.

Spatio-Temporal Attention Networks for Action Recognition and Detection

2020 ◽  
Vol 22 (11) ◽  
pp. 2990-3001 ◽  
Author(s):  
Jun Li ◽  
Xianglong Liu ◽  
Wenxuan Zhang ◽  
Mingyuan Zhang ◽  
Jingkuan Song ◽  
...  
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Networks ◽  
Spatio Temporal

Unified Spatio-Temporal Attention Networks for Action Recognition in Videos

2019 ◽  
Vol 21 (2) ◽  
pp. 416-428 ◽  
Author(s):  
Dong Li ◽  
Ting Yao ◽  
Ling-Yu Duan ◽  
Tao Mei ◽  
Yong Rui
Keyword(s):  
Action Recognition ◽  
Temporal Attention ◽  
Attention Networks ◽  
Spatio Temporal
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