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.

News

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.

March, 2017 : A paper on ‘Efficient 2D and 3D Facade Segmentation’ is accepted to PAMI’17.

March, 2017 : A paper on ‘Video Propagation Networks’ is accepted to CVPR’17.

October, 2016 : Received Outstanding Reviewer award at ECCV’16.

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(firstname)(lastname)@gmail.com
Nvidia Research
2 Technology Park Drive
01886, Westford, USA.

Recent Publications/Reports

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 coming soon

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)


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Theses

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

pdf