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.


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.

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.

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Nvidia Research
2 Technology Park Drive
01886, Westford, USA.


SPLATNet: Sparse Lattice Networks for Point Cloud Processing

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)

arxiv pre-print / video / code (coming soon)

Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation

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)

arxiv pre-print / code (coming soon)

Learning Superpixels with Segmentation-Aware Affinity Loss

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

pdf (coming soon) / code (coming soon)

Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization

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

arxiv pre-print

Semantic Video CNNs through Representation Warping

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)

pdf / video / code (github) / ICCV talk / poster

Video Propagation Networks

A fast, generic and end-to-end trainable approach for propagating information across video frames.

V. Jampani, R. Gadde and P. V. Gehler

Computer Vision and Pattern Recognition, CVPR’17

pdf / supplementary / project page / code (github) / poster

Efficient 2D and 3D Facade Segmentation using Auto-Context

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

arXiv pre-print / code (bitbucket)

Superpixel Convolutional Networks using Bilateral Inceptions

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

pdf / supplementary / project page / code (github) / poster

Learning Sparse High-Dimensional Filters: Image Filtering, Dense CRFs and Bilateral Neural Networks

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

pdf / supplementary / project page / code (github) / poster

Optical Flow with Semantic Segmentation and Localized Layers

An approach for incorporating semantics of the scene for better optical flow estimation.

L. Sevilla, D. Sun, V. Jampani and M. J. Black

Computer Vision and Pattern Recognition, CVPR’16

pdf / video / project page / code (zip)

Permutohedral Lattice CNNs

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


Consensus Message Passing for Layered Graphical Models

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

pdf / supplementary / arXiv

Efficient Facade Segmentation using Auto-Contex

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

pdf / supplementary / code (bitbucket) / project page

The Informed Sampler: A Discriminative Approach to Bayesian Inference in Generative Computer Vision Models

A generic MCMC sampling technique for inverting graphic engines.

V. Jampani, S. Nowozin, M. Loper and P. V. Gehler

Computer Vision and Image Understanding Journal, CVIU’15

pdf / arXiv

Assessment of Computational Visual Attention Models on Medical Images

Analysis and extensions of visual attention models for medical image perception.

V. Jampani, Ujjwal, J. Sivaswamy and V. Vaidya

Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP’12

pdf / online

Role of Expertise and Contralateral Symmetry in the Diagnosis of Pneumoconiosis: An Experimental Study

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


ImageFlow: Streaming Image Search

A novel image search user interface that explores a different alternative to the traditional grid interfaces.

V. Jampani, G. Ramos and S. Drucker

Microsoft Research Technial Report, Redmond, MSR-TR-2010-148

pdf / news article

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Learning Inference Models for Computer Vision

Learning based techniques for better inference in several computer vision models ranging from inverse graphics to freely parameterized neural networks.

V. Jampani

PhD Thesis, MPI for Intelligent Systems and University of Tübingen, December, 2016

pdf / slides / library

A Study of X-Ray Image Perception for Pneumoconiosis Detection

Eye tracking experimental studies and models for X-Ray Image Perception and Diagnosis.

V. Jampani

Master Thesis, IIIT-Hyderabad, January, 2013