Bridging the Gap Between Computational Photography and Visual Recognition:
5th UG2+ Prize Challenge
CVPR 2022
$10K prizes

The rapid development of computer vision algorithms increasingly allows automatic visual recognition to be incorporated into a suite of emerging applications. Some of these applications have less-than-ideal circumstances such as low-visibility environments, causing image captures to have degradations. In other more extreme applications, such as imagers for flexible wearables, smart clothing sensors, ultra-thin headset cameras, implantable in vivo imaging, and others, standard camera systems cannot even be deployed, requiring new types of imaging devices. Computational photography addresses the concerns above by designing new computational techniques and incorporating them into the image capture and formation pipeline. This raises a set of new questions. For example, what is the current state-of-the-art for image restoration for images captured in non-ideal circumstances? How can inference be performed on novel kinds of computational photography devices?

Continuing the success of the 1st (CVPR'18), 2nd (CVPR'19), 3rd (CVPR'20), and 4th (CVPR'21) UG2 Prize Challenge workshops, we provide its 5th version for CVPR 2021. It will inherit the successful benchmark dataset, platform and evaluation tools used by the first three UG2 workshops, but will also look at brand new aspects of the overall problem, significantly augmenting its existing scope.

Original high-quality contributions are solicited on the following topics:
  • Novel algorithms for robust object detection, segmentation or recognition on outdoor mobility platforms (UAVs, gliders, autonomous cars, outdoor robots etc.), under real-world adverse conditions and image degradations (haze, rain, snow, hail, dust, underwater, low-illumination, low resolution, etc.)
  • Novel models and theories for explaining, quantifying, and optimizing the mutual influence between the low-level computational photography tasks and various high-level computer vision tasks, and for the underlying degradation and recovery processes, of real-world images going through complicated adverse visual conditions.
  • Novel evaluation methods and metrics for image restoration and enhancement algorithms, with a particular emphasis on no-reference metrics, since for most real outdoor images with adverse visual conditions it is hard to obtain any clean “ground truth” to compare with.


NVIDIA Walmart

Challenge Categories



Awarded in prizes

Keynote speakers

Available Challenges

What is the current state-of-the-art for image restoration for images captured in non-ideal circumstances? How can inference be performed on novel kinds of computational photography devices?

The UG2+ Challenge seeks to advance the analysis of "difficult" imagery by applying image restoration and enhancement algorithms to improve analysis performance. Participants are tasked with developing novel algorithms to improve the analysis of imagery captured under problematic conditions.

Object Detection in Haze

While most current vision systems are designed to perform in environments where the subjects are well observable without (significant) attenuation or alteration, a dependable vision system must reckon with the entire spectrum of complex unconstrained and dynamic degraded outdoor environments. It is highly desirable to study to what extent, and in what sense, such challenging visual conditions can be coped with, for the goal of achieving robust visual sensing.

Our challenge is based on the A2I2-Haze, the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery.

Semi-supervised Action Recognition from Dark Videos

Action recognition has been applied in various fields, yet most current action recognition methods focus on videos shot under normal illumination. In the meantime, there are various scenarios where videos shot under adverse illumination is unavoidable, such as night surveillance, and self-driving at night. It is therefore highly desirable to explore possible methods that are robust and could cope with these scenarios. It would be event better if such methods could utilize web videos, which are widely available and normally shot under normal illumination.

Atmospheric Turbulence Mitigation

The theories of turbulence and propagation of light through random media have been studied for the better part of a century. Yet progress for associated image reconstruction algorithms has been slow, as the turbulence mitigation problem has not thoroughly been given the modern treatments of advanced image processing approaches (e.g., deep learning methods) that have positively impacted a wide variety of other imaging domains (e.g., classification).

This challenge aims to promote the development of new image reconstruction algorithms for incoherent imaging through anisoplanatic turbulence.

Keynote speakers

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Ming-Hsuan Yang
University of California, Merced
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Danna Gurari
University of Colorado Boulder
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Xiaohua Zhai
Google Brain
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Achuta Kadambi
University of California, Los Angeles
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Ulugbek Kamilov
Washington University in St. Louis
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Anqi (Angie) Liu
Johns Hopkins University
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Qifeng Chen
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Daniel LeMaster
Air Force Research
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Russell Hardie
University of Dayton

Important Dates

Challenge Registration

January 15 - May 1, 2022

Challenge Dry-run

January 15 - May 1, 2022

Paper Submission Deadline

March 22, 2022

Notification of Paper Acceptance

March 30, 2022

Paper Camera Ready

April 2, 2022

Challenge Final Result Submission

April 30 - May 1, 2022

Challenge Winners Announcement

May 20, 2022

CVPR Workshop

June 19, 2022

Advisory Committee

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Zhangyang Wang
University of Texas, Austin
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Jiaying Liu
Peking University
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Walter J. Scheirer
University of Notre Dame
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Stanley H. Chan
Purdue University
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Wenqi Ren
Chinese Academy of Sciences
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Shalini De Mello
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Keigo Hirakawa
University of Dayton

Organizing Committee

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Wuyang Chen
University of Texas, Austin
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Wenhan Yang
City University of Hong Kong, Hong Kong
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Yuecong Xu
Nanyang Technological University, Singapore
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Zhenghua Chen
Institute for Infocomm Research (I2R), A*STAR, Singapore
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Zhenyu Wu
Wormpex AI Research
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Zhiyuan Mao
Purdue University
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Nicholas Chimitt
Purdue University