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Video Computing Group

The Video Computing Group at the University of California, Riverside conducts research on the foundations and applications of computer vision, image processing, and statistical learning.

Principal Investigator: Amit K. Roy-Chowdhury

Current projects are related to camera networks, tracking and pose estimation, event recognition and prediction, efficient learning and inference, multi-modal data analysis, adversarial attacks/defenses in vision systems, integrated sensing and navigation, distributed machine learning, and bio-image analysis. The work provides the scientific underpinnings behind applications in cyber-physical, autonomous and intelligent systems. Some of the application areas include environmental monitoring, agriculture, and biology/medicine. Members of the group regularly publish in top-tier conferences and journals in computer vision, machine learning, and image processing. Past members work in major research labs and hold faculty positions across the world.

Open Positions

The Video Computing Group is looking for highly motivated and talented graduate and undergraduate students. If interested, prospective graduate students should check here for ECE students and here for CSE students. Interested undergraduate students should directly email the PI. Post-doc positions will be announced when available.

Latest News

Three papers accepted to ICCV 2023

VCG at UCR students are first authors on three papers accepted to ICCV 2023.

Amazon Research Award

Amit Roy-Chowdhury recently received an Amazon Research Award on "Exploring privacy in deep metric learning: applications in computer vision".

CVPR 2023 Paper On Unbiased Scene Graph Generation in Videos

Our paper 'unbiased scene graph generation in videos' is accepted in CVPR'23. The work addresses the critical problem of biased visual relationship detection in videos.

Three Papers in NeurIPS 2022

Video Computing Group members have three papers in NeurIPS 2022. The topics are "generative adversarial attacks using vision-language models", "audio-visual-language embodied navigation (with MERL)" and "blackbox attack via surrogate ensemble search".

Two Papers in ECCV 2022

Video Computing Group members have two papers in ECCV 2022. One paper is on cross-modal knowledge transfer which is an oral (collaboration with MERL) and another on how to temporally localize video moments based on text queries

Paper accepted to MICCAI 2022

Paper on self-supervised poisson denoising from a single image accepted at MICCAI 2022


The Video Computing Group gratefully acknowledges the support received from a number of government agencies and private corporations.

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