The advantages of collecting images from outdoor camera platforms, like UAVs, surveillance cameras and outdoor robots, are evident and clear. For instance, man-portable UAV systems can be launched from safe positions to survey difficult or dangerous terrain, acquiring hours of video without putting human lives at risk. What is unclear is how to automate the interpretation of these images - a necessary measure in the face of millions of frames containing artifacts unique to the operation of the sensor and optics platform in outdoor, unconstrained, and usually visually degraded environments.

Continuing the success of the 1st UG² Prize Challenge workshop held at CVPR 2018, UG²+ provides an integrated forum for researchers to review the recent progress of handling various adverse visual conditions in real-world scenes, in robust, effective and task-oriented ways. Beyond the human vision-driven restorations, we also extend particular attention to the degradation models and the related inverse recovery processes that may benefit successive machine vision tasks. We embrace the most advanced deep learning systems, but are still open to classical physically grounded models, as well as any well-motivated combination of the two streams. The workshop will consist of four invited talks, together with peer-reviewed regular papers (oral and poster), and talks associated with winning prize challenge contributions.

Original high-quality contributions are solicited on the following topics:

• 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.