I am a research scientist at Nvidia Research in Westford, US. Prior to joining Nvidia, I completed my PhD at Perceiving Systems department, Max-Planck Institute (MPI) for Intelligent Systems in Tübingen, Germany. My PhD advisor was Prof. Peter V. Gehler. I did my bachelors and masters in Computer Science at IIIT-Hyderabad, India.
My work lies at the intersection of Computer Vision and Machine Learning. Specifically, I am working on leveraging machine learning techniques for better inference in computer vision models. The main research question is how to make use of learning techniques such as deep neural networks and random forests for inference in structured prediction frameworks.
February, 2018 : Papers on point-cloud CNNs (oral), multi-frame video interpolation (spotlight) and superpixels with deep features are accepted to CVPR’18.
October, 2017 : Code for ‘Semantic Video CNNs’ is released.
October, 2017 : Talk on ‘Bilateral Neural Networks’ at University of Massachusetts, Amherst.
July, 2017 : A paper on ‘Semantic Video CNNs’ is accepted (oral) to ICCV’17.
July, 2017 : Received Outstanding Reviewer award at CVPR’17.
July, 2017 : Joined as a research scientist in Jan Kautz’s group at Nvidia Research.
May, 2017 : Received PhD (summa cum laude) from University of Tübingen, Germany.
April, 2017 : Code for ‘Video Propagation Networks’ is released.
2 Technology Park Drive
01886, Westford, USA.
A fast and end-to-end trainable neural network that directly works on point clouds and can also do joint 2D-3D processing.
H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M-H. Yang and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18 (oral)
Variable-length multi-frame video interpolation via self-supervised optical flow estimation and occlusion reasoning.
H. Jiang, D. Sun, V. Jampani, M-H. Yang, E. Learned-Miller and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18 (spotlight)
A learning technique that leverages deep networks to predict pixel affinities useful for graph-based superpixel segmentation.
W-C. Tu, M-Y. Liu, V. Jampani, D. Sun, S-Y. Chien, M-H. Yang and J. Kautz
Computer Vision and Pattern Recognition, CVPR’18
A fast and end-to-end trainable approach for converting image CNNs to video CNNs for semantic segmentation.
R. Gadde, V. Jampani and P. V. Gehler
International Conference on Computer Vision, ICCV’17 (oral)
A fast auto-context based facade segmentation approach for segmenting both 2D images and 3D point clouds.
R. Gadde*, V. Jampani*, R. Marlet and P. V. Gehler (*equal contribution)
Pattern Analysis and Machine Intelligence, PAMI’17
An inception style fast bilateral filtering module that can be used in existing image segmentation CNNs.
R. Gadde*, V. Jampani*, M. Kiefel, D. Kappler and P. V. Gehler (*equal contribution)
European Conference on Computer Vision, ECCV’16
Parameterization and learning of sparse high-dimensional filters with wide range of applications in image filtering, CRFs and neural networks.
V. Jampani, M. Kiefel and P. V. Gehler
Computer Vision and Pattern Recognition, CVPR’16
CNNs with high-dimensional permutohedral kernels instead of standard spatial kernels.
M. Kiefel, V. Jampani and P. V. Gehler
International Conference on Learning Representations, ICLR Workshops’15
Learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks.
PhD Thesis, MPI for Intelligent Systems and University of Tübingen, December, 2016
Eye tracking experimental studies and models for X-Ray Image Perception and Diagnosis.
Master Thesis, IIIT-Hyderabad, January, 2013