Learning a non-linear knowledge transfer model for cross-view action recognition

By Hossein Rahmani, Ajmal Saeed Mian. 2015

155 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Meta & Continual Learning, Human-Computer Interaction

How researchers see this paper

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

In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Representation & Self-Supervised Learning

In Meta & Continual Learning

In Human-Computer Interaction

Citations in the corpus

NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis (2016, background): “Rahmani and Mian [29] introduced a nonlinear knowledge transfer model to transform different views of human actions to a canonical view.”
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding (2019, background): “Rahmani and Mian [83] introduced a nonlinear knowledge transfer model to transform different views of the human actions to a canonical view for action classiﬁcation.”
Deep Multimodal Feature Analysis for Action Recognition in RGB+D Videos (2016, background): “Rahmani and Mian [48] introduced a nonlinear knowledge transfer model to transform different views of human actions to a canonical view.”
Space-Time Representation of People Based on 3D Skeletal Data: A Review (2016, background): “Rahmani and Mian [177] Knowledge Transfer BoW Lowlv Dict X”
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points (2018, neutral): “0 NKTM [42] V 75.”
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].”
View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017, background): “Researchers have paid much attention to this issue and designed view-invariant approaches for action recognition from color videos [16, 30, 3, 32, 20, 7, 33, 41, 23, 15, 22, 43, 44, 25, 49, 29, 8].”
Sim2real transfer learning for 3D human pose estimation: motion to the rescue (2019, background): “Other works consider pedestrian detection [59–61] and action recognition [62, 63].”
A Review of Human Activity Recognition Methods (2015, background): “Multiview action recognition has also been studied by Rahmani and Mian (2015).”
Real-Time Online Action Detection Forests Using Spatio-Temporal Contexts (2016, background): “The state-of-the-art convolutional neural network (CNN)-based features [22, 6] extracted from RGB and depth images have demonstrated greater robustness in action recognition [34, 33, 8, 39].”
Learning a Deep Model for Human Action Recognition from Novel Viewpoints (2016, methodology): “Per class recognition accuracy of our proposed R-NKTM and NKTM [43] on the IXMAS [36] dataset.”
Learning from Synthetic Humans (2017, methodology): “In [30], action recognition is addressed with synthetic human trajectories from MoCap data.”
3D Action Recognition from Novel Viewpoints (2016, methodology): “Experiments on two multiview and three single-view benchmark datasets show that the proposed method dramatically outperforms existing state-of-the-art in action recognition.”
Deep Domain Generalization With Structured Low-Rank Constraint (2018, background): “…and increased great attention in a lot of real-world applications, due to its promising ability in different learning tasks, e.g., face recognition [32], image super-resolution [25], action classiﬁcation [33], [34], feature selection [35], image clustering [36] and multi-view learning [37].”
Going deeper into action recognition: A survey (2016, background): “For actions, even stronger connections between spatial and temporal cues exist (Rahmani and Mian, 2015).”
View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018, background): “Some attempts have been made in previous works to overcome the view variation problem for robust action recognition [3], [8], [9], [14], [15], [19], [24], [26], [31], [36], [37], [39], [40], [50], [52], [53], [60].”