Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action Recognition
By Junwu Weng, Chaoqun Weng, Junsong Yuan. 2017
115 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jun Liu
- Alex ChiChung Kot
- Ling-yu Duan
- G. Wang
- Amir Shahroudy
- Mengyuan Liu
- Junsong Yuan
- Hossein Rahmani
In Biomedical Engineering
- G. Wang
- Jun Liu
- Ling-yu Duan
- Alex ChiChung Kot
- Amir Shahroudy
- Junsong Yuan
- Mengyuan Liu
- Hossein Rahmani
In Computer Vision and Pattern Recognition
- Jun Liu
- G. Wang
- Alex ChiChung Kot
- Ling-yu Duan
- Amir Shahroudy
- Mengyuan Liu
- Junsong Yuan
- Hossein Rahmani
Citations in the corpus
- Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates (2017, neutral): “Skeleton-based action recognition has been explored in different aspects in recent years [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57].”
- SSNet: Scale Selection Network for Online 3D Action Prediction (2018, background): “Most of the existing 3D action recognition methods [63, 51, 57, 69, 24, 66, 10, 29] take fully observed segmented videos as input (each sample contains one full action instance), and output a class label.”
- Recognizing Human Actions as the Evolution of Pose Estimation Maps (2018, background): “Hand-crafted features [42, 47, 13] were designed for describing evolution of 3D poses.”