Lab Group

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, event recognition and prediction, learning with limited supervision, resource-constrained data analysis, integrated sensing and navigation, and bio-image analysis. The work provides the scientific underpinnings behind applications in cyber-physical, autonomous and intelligent systems. Members of the group regularly publish in top-tier conferences and journals in computer vision 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

New monograph on person re-identification with limited supervision

An overview of existing literature focusing on some specific problems in the context of person re-identification with limited supervision in multi-camera environments.

Three recent papers in NeurIPS 2021, ICCV 2021, and AAAI 2022 on adversarial attacks

Papers on adversarial attacks on black-box video classifiers accepted at NeurIPS 2021, defense against adversarial attacks on complex scene images accepted at ICCV 2021, and context-aware transfer attacks for object detection accepted at AAAI 2022

New Projects on Robot Autonomy

VCG researchers are involved in two projects related to machine learning for robot autonomy.

Paper on Moment Localization from Video Corpus accepted in IEEE T-IP 2021

Instead of traditional text-based moment localization from a given video, this paper addresses a realistic and challenging problem of text-based localization of moments in a video corpus.

Oral presentations at CVPR, ICML and ACM MM 2021

Oral papers on unsupervised multi-source domain adaptation without source data at CVPR 2021, on cross-domain imitation learning from observations at ICML 2021, and on adaptive super-resolution at ACM Multimedia 2021

Three papers from VCG Members accepted at ICCV 2021

Papers on supervised video person re-identification and adversarial attacks accepted at ICCV 2021


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