Lab Group
header-image_0001_layer_4.jpg
header-image_0003_layer_0.jpg
header-image_0002_layer_1.jpg

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

Two Papers in CVPR 2022

Video Computing Group members have two papers in CVPR 2022. One paper is on designing dynamic multi-task architectures which is an oral (collaboration with NEC Labs) and another on context-aware adversarial attacks (collaboration with PARC)

Three papers on context-aware adversarial attacks in AAAI 2022, CVPR 2022, and NeurIPS 2021

Our recent work has shown how to develop adversarial attacks that are aware of the contextual relationships among multiple objects in a scene and presented at major conferences: NeurIPS, AAAI and CVPR.

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.

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

Acknowledgements

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

Acknowledgement Logos