Sudheendra Vijayanarasimhan
Sudheendra received his Ph.D. in Computer Science from the University of Texas at Austin in 2011 where his research focused on Active learning in Computer Vision and joined Google in 2011. His current work at Google Research is focused on Video Classification and Action Recognition.
His past projects include scaling up object detection and neural networks classification and the design of neural-network architectures for video classification.
Research Areas
Authored Publications
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Google
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
Carl Martin Vondrick
Jitendra Malik
CVPR (2018)
Rethinking the Faster R-CNN Architecture for Temporal Action Localization
Jia Deng
Yu-Wei Chao
CVPR 2018
Self-Supervised Learning of Structure and Motion from Video
Aikaterini Fragkiadaki
arxiv (2017)
End-to-End Learning of Semantic Grasping
Eric Jang
Julian Ibarz
Peter Pastor Sampedro
Sergey Levine
CoRL 2017 (2017) (to appear)
The Kinetics Human Action Video Dataset
Andrew Zisserman
Joao Carreira
Karen Simonyan
Will Kay
Brian Zhang
Chloe Hillier
Fabio Viola
Tim Green
Trevor Back
Mustafa Suleyman
arXiv (2017)
YouTube-8M: A Large-Scale Video Classification Benchmark
Nisarg Kothari
Joonseok Lee
Balakrishnan Varadarajan
arXiv:1609.08675 (2016)
Efficient Large Scale Video Classification
Balakrishnan Varadarajan
dblp computer science bibliography, http://dblp.org (2015) (to appear)
Deep Networks With Large Output Spaces
Jonathon Shlens
Rajat Monga
International Conference on Learning Representations (2015)
Beyond Short Snippets: Deep Networks for Video Classification
Joe Yue-Hei Ng
Matthew Hausknecht
Rajat Monga
Computer Vision and Pattern Recognition (2015)
Fast, Accurate Detection of 100,000 Object Classes on a Single Machine
Thomas Dean
Mark Ruzon
Mark Segal
Jonathon Shlens
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington, DC, USA (2013)
