Hierarchical motion evolution for action recognition

By Hongsong Wang, Liang Wang. 2015

6 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Jun Liu
2. G. Wang
3. Amir Shahroudy
4. Alex ChiChung Kot
5. Hossein Rahmani
6. Qiuhong Ke
7. Dong Xu
8. Pichao Wang

Citations in the corpus

• Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, methodology): “As shown in Table 8, our method surpasses the state-ofthe-art methods [39], [91], [92], [93], [94], [95], which demonstrates the effectiveness of our method in dealing with skeleton-based action recognition problem.”
• Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks (2017, neutral): “Precision Recall F1-score Skeleton Feature [48] 59.9 59.3 59.6 Portfolios [52] – – 56.0 Gesture Spotting [35] 61.2 62.3 61.7 HiVideoDarwin [45] 74.9 75.6 74.6 CNN for Skeleton [5] 91”