View-Invariant Action Recognition Using Latent Kernelized Structural SVM
By Xinxiao Wu, Yunde Jia. 2012
70 citations recorded by the source corpus.
Subfields: 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 Biomedical Engineering
- Nanning Zheng
- Pengfei Zhang
- Jianru Xue
- Cuiling Lan
- Wenjun Zeng
- Junliang Xing
- Zhuolin Jiang
- Jingjing Zheng
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Ming-Hsuan Yang
- Xiaogang Wang
- Dacheng Tao
- Yi Yang
- G. Wang
- Trevor Darrell
- Jitendra Malik
Citations in the corpus
- 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].”
- Cross-View Action Recognition via Transferable Dictionary Learning (2016, background): “The second one is MIXSVM which trains two SVM’s on the training and testing views and learns an optimal linear combination of them.”
- 3D Pose from Motion for Cross-View Action Recognition via Non-linear Circulant Temporal Encoding (2014, unknown): “Other recent methods include [33], where viewpoint is treated as a latent variable in a struc-”
- Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach (2013, background): “However, both [20, 18] assumed simple models with few latent variables that could be enumerated during inference.”
- 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].”
- Latent Multitask Learning for View-Invariant Action Recognition (2013, background): “The transformation is learned on the co-occurrence statistics of view-dependent features.”
- Cross-View Action Recognition over Heterogeneous Feature Spaces (2013, background): “[15] proposed a latent kernelized structural SVM for view-invariant action recognition where the view is modeled as a latent variable and inferred during both training and testing stage.”