The 6th UG2+ Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition.

In conjunction with CVPR 2023, June 19

Website: http://www.ug2challenge.org/

Contact: cvpr2023.ug2challenge@gmail.com

Track 1: 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.This challenge is based on the A2I2-Haze, the first real haze dataset with in-situ smoke measurement aligned to aerial and ground imagery.

Track 2: 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.

Track 3: Single Image Deraining

Images captured in adverse weather conditions significantly impact the performance of many vision tasks. Rain is a common weather phenomenon that introduces visual degradations to captured images and videos through partial occlusions of objects – in heavy rain, severe occlusion to the background. As most vision algorithms assume clear weather, with no interference of rain, their performance suffers. Deraining is the task of removing such visual degradations so that the images are better suited to the assumptions of downstream vision algorithms, as well as for aesthetic fruition.
This challenge aims to spark innovative ideas that will push the envelope of single image deraining on real images.

Paper Track:

• Novel algorithms for robust object detection, segmentation or recognition on outdoor mobility platforms, such as UAVs, gliders, autonomous cars, outdoor robots, etc.
• Novel algorithms for robust object detection and/or recognition in the presence of one or more real-world adverse conditions, such as haze, rain, snow, hail, dust, underwater, low-illumination, low resolution, etc.
• The potential models and theories for explaining, quantifying, and optimizing the mutual influence between the low-level computational photography (image reconstruction, restoration, or enhancement) tasks and various high-level computer vision tasks.
• Novel physically grounded and/or explanatory models, 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.

Submission: https://cmt3.research.microsoft.com/UG2CHALLENGE2023

Special This Year!

The 2023 UG2+ workshop will partner with IEEE Transactions on Computational Imaging to bridge the computer vision and computational imaging community. Our objective is to strengthen synergy across the communities by providing UG2+ authors with an opportunity to publish in a journal with an expedited review process.

Authors of the workshop proceedings (8-pages) can indicate in the CMT submission page whether they would like the paper to be considered for IEEE Transactions on Computational Imaging. A paper cannot be simultaneously published as a workshop proceeding and a journal.

• Authors of the workshop proceedings will have a choice in the CMT submission page to indicate if they would like the paper to be considered for publishing at IEEE Transactions on Computational Imaging. UG2+ paper reviewers and workshop chairs will identify high-quality papers. In consultation with the TCI editorial board, we will make recommendations to the shortlisted paper.
• For papers recommended to TCI, authors will be notified of additional instructions including reformatting into the TCI format (10 pages) and submitting the files to ScholarOne website. Suggestions from the TCI editorial board will be given to assist authors so that the journal review will be expedited.
• For papers that indicate workshop proceedings OR not shortlisted by TCI, the publication decision will be solely based on UG2+ workshop criteria.
• Our shortlisting criteria follows the IEEE Signal Processing Society publication requirement. While we cannot guarantee acceptance to the journal ultimately, shortlisted papers are meant to pass the screening of the TCI editorial board with positive recommendations. The final journal decision will be made by the editor-in-chief, Professor Mujdat Cetin.

Important Dates:

• Paper submission: March 22, 2023 (11:59PM PST)
• Camera ready deadline: April 2, 2023 (11:59PM PST)
• Challenge result submission: May 1, 2023 (11:59PM PST)
• Winner Announcement: May 25, 2023 (11:59PM PST)
• CVPR 2023 Workshop: June 19, 2023 (Full day)

Speakers:

• Jong Chul Ye (Korea Advanced Institute of Science & Technology)
• Sabine Süsstrunk (EPFL)
• Jinwei Gu (SenseBrain)
• Vishal M. Patel (John Hopkins University)
• Nianyi Li  (Clemson University)
• Tianfan Xue (The Chinese University of Hong Kong)
• Emma Alexander (North Western University)
• Kevin J. Miller (US Army)

Organizers:

• Zhiyuan Mao (Purdue University)
• Stanley H. Chan (Purdue University)
• Wuyang Chen (UT Austin)
• Zhangyang Wang (UT Austin)
• Howard Zhang (University of California, Los Angeles)
• Yunhao Ba (University of California, Los Angeles)
• Achuta Kadambi (University of California, Los Angeles)

• Alex Wong (Yale University)
• Ajay Jaiswal (UT Austin)
• Abdullah Al-Shabili (Purdue University)
• Xingguang Zhang (Purdue University)
• Zhenyu Wu (Wormpex AI Research)
• Kevin J. Miller (US Army)
• Jiaying Liu (Peking University)
• Walter J. Scheirer (University of Notre Dame)
• Wenqi Ren (Chinese Academy of Sciences)