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Rahul Rama Varior
Rahul Rama Varior
Computer Vision, Deep Learning, Machine Learning Researcher
Verified email at e.ntu.edu.sg
Title
Cited by
Cited by
Year
Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification
R Rama Varior, M Haloi, G Wang
European Conference on Computer Vision, 2016
954*2016
A Siamese Long Short-Term Memory Architecture for Human Re-Identification
R Rama Varior, B Shuai, J Lu, D Xu, G Wang
European Conference on Computer Vision, 2016
704*2016
Learning invariant color features for person reidentification
RR Varior, G Wang, J Lu, T Liu
IEEE Transactions on Image Processing 25 (7), 3395-3410, 2016
992016
Scale-aware attention network for crowd counting
RR Varior, B Shuai, J Tighe, D Modolo
arXiv preprint arXiv:1901.06026 1 (2), 3, 2019
312019
Multi-scale attention network for crowd counting
RR Varior, B Shuai, J Tighe, D Modolo
arXiv preprint arXiv:1901.06026, 2019
312019
Understanding the impact of mistakes on background regions in crowd counting
D Modolo, B Shuai, RR Varior, J Tighe
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
272021
A data-driven color feature learning scheme for image retrieval
RR Varior, G Wang
2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015
42015
Real-time Driver Monitoring Systems on Edge AI Device
J Hariharan, RR Varior, S Karunakaran
arXiv preprint arXiv:2304.01555, 2023
22023
Visual tracking using learned color features
T Liu, RR Varior, G Wang
2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015
22015
Multi-Scale Attention Network for Crowd Counting
R Rama Varior, B Shuai, J Tighe, D Modolo
arXiv e-prints, arXiv: 1901.06026, 2019
12019
Hierarchical invariant feature learning with marginalization for person re-identification
RR Varior, G Wang
arXiv preprint arXiv:1511.09150, 2015
12015
Hierarchical invariant feature learning with marginalization for person re-identification
R Rama Varior, G Wang
ArXiv e-prints, arXiv: 1511.09150, 2015
12015
Learning representations for human re-identification
RR Varior
2017
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