3rd UG2+ Workshop and Prize Challenge: Bridging the Gap between Computational Photography and Visual Recognition Call for Papers

Call for Challenge Participants & Call for Papers

The 3rd UG2+ Workshop and Prize Challenge: Bridging the Gap between
Computational Photography and Visual Recognition.
In conjunction with CVPR 2020, June 19, Seattle, USA.

Website: http://cvpr2020.ug2challenge.org/index.html
Contact: cvpr2020.ug2challenge@gmail.com

Track 1: Object Detection in Poor Visibility Environments [Register:
https://forms.gle/dceUY9hyEsBADzuM6] 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.
1) Object Detection in the Hazy & Rainy Condition
2) Face Detection in the Low-Light Condition
3) Sea Life Detection in the Underwater Condition

Track 2: Face Verification on FlatCam Images [Register:
https://forms.gle/qmgESBvqA2pPEq28A] Despite the easy integration into
numerous computer vision applications, FlatCam lensless camera images
contain noise and artifacts unseen in standard lens-based cameras,
which degrades its performance. This track explores new algorithms to
better integrate lensless cameras into the face verification task.
1) Image Enhancement for FlatCam Face Verification
2) Image Reconstruction for FlatCam Face Verification
3) End-to-End Face Verification on FlatCam Measurements

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/UG2CHALLENGE2020

Important Dates:
 Paper submission: March 20, 2020 (11:59PM PST)
 Challenge result submission: April 8, 2020 (11:59PM PST)
 Winner & Paper Announcement: April 10, 2020 (11:59PM PST)
 Camera ready deadline: April 16, 2020 (11:59PM PST)
 CVPR Workshop: June 19, 2020 (Full day)

 Judy Hoffman (Georgia Institute of Technology)
 Xiaoming Liu (Michigan State University)
 Vishal M. Patel (Johns Hopkins University)
 Zhiding Yu (NVIDIA)
 Dengxin Dai (ETH Zurich)
 Bihan Wen (Nanyang Technological University (NTU), Singapore)
 Honghui Shi (University of Oregon)
 Xi Yin (Microsoft Cloud and AI)

 Zhangyang Wang (Texas A&M University)
 Walter J. Scheirer (University of Notre Dame)
 Ashok Veeraraghavan (Rice University)
 Jiaying Liu (Peking University)
 Risheng Liu (Dalian University of Technology)
 Wenqi Ren (Chinese Academy of Sciences)
 Wenhan Yang (City University of Hong Kong, Hong Kong)
 Yingyan Lin (Rice University)
 Ye Yuan (Texas A&M University)
 Jasper Tan (Rice University)
 Wuyang Chen (Texas A&M University)