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.
I am looking for motivated students for collaborations and internships. If interested, please drop me an email with your CV.
September, 2018 : Code for ‘Superpixel Sampling Networks’ is released.
August, 2018 : Code for ‘Learning Superpixels with Segmentation-Aware Loss’ is released.
July, 2018 : Papers on ‘Superpixel Sampling Networks’ and ‘Switchable Temporal Propagation Networks’ accepted to ECCV’18.
June, 2018 : Our work on SPLATNet received best paper honorable mention at CVPR’18.
June, 2018 : Code for ‘SPLATNet’ is released.
April, 2018 : Received Outstanding Reviewer award at CVPR’18.
April, 2018 : A paper on training deep networks with synthetic data is accepted to ‘Workshop on autonomous driving’ at CVPR’18.
February, 2018 : Papers on point-cloud CNNs (oral), multi-frame video interpolation (spotlight) and superpixels with deep features are accepted to CVPR’18.
2 Technology Park Drive
01886, Westford, USA.
An end-to-end trainable deep superpixel algorithm that allows learning with flexible loss functions resulting in the learning of task-specific superpixels.
V. Jampani, D. Sun, M-Y. Liu, M-H. Yang and J. Kautz
European Conference on Computer Vision, ECCV’18
A generic fast deep learning framework for propagating a variety of visual properties (such as color, HDR, segmentation) across video frames.
S. Liu, G. Zhong, S. D. Mello, J. Gu, V. Jampani, M-H. Yang and J. Kautz
European Conference on Computer Vision, ECCV’18
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, best paper honorable mention)
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 study exploring the use of domain randomization for real-world computer vision tasks such as object detection.
J. Tremblay*, A. Prakash*, D. Acuna*, M. Brophy*, V. Jampani, C. Anil, T. To, E. Cameracci, S. Boochoon and S. Birchfield (*equal contribution)
Workshop on Autonomous Driving, CVPR-Workshops’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
Improved message passing inference in layered vision models that can be used in probabilistic programs like Infer.NET.
V. Jampani*, S. M. A. Eslami*, D. Tarlow, P. Kohli and J. Winn (*equal contribution)
Artificial Intelligence and Statistics, AISTATS’15
A state-of-the-art approach for facade segmentation that is faster than most existing approaches.
V. Jampani*, R. Gadde* and P. V. Gehler (*equal contribution)
Winter Conference on Applications of Computer Vision, WACV’15
An experimental eye-tracking study on X-ray image perception, with observers ranging from novices to experienced radiologists.
V. Jampani, V. Vaidya, J. Sivaswamy and K. L. Tourani
SPIE, Medical Imaging’11
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