Low-Rank Tensor Subspace Learning for RGB-D Action Recognition

By Chengcheng Jia, Yun Raymond Fu. 2016

45 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

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Yuyao Zhang
7. Leyuan Fang
8. José M. Bioucas-Dias

In Computer Vision and Pattern Recognition

1. Pichao Wang
2. Wanqing Li
3. Philip O. Ogunbona
4. Zhimin Gao
5. Chang-Fu Tang
6. Leyuan Fang
7. Renwei Dian
8. Shutao Li

Citations in the corpus

• Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks (2017, background): “Previous works [28, 16, 10, 58, 61, 13] showed the effectiveness of fusing the two modalities for 3D action recognition.”
• Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization (2018, methodology): “Recently, tensor-based methods have been successfully applied to multi-frame data restoration [31]–[34], recognition [35], [36], and unmixing [37], etc.”