This report reviews the results of the GT-Rain challenge on single image
deraining at the UG2+ workshop at CVPR 2023. The aim of this competition is to
study the rainy weather phenomenon in real world scenarios, provide a novel
real world rainy image dataset, and to spark innovative ideas that will further
the development of single image deraining methods on real images. Submissions
were trained on the GT-Rain dataset and evaluated on an extension of the
dataset consisting of 15 additional scenes. Scenes in GT-Rain are comprised of
real rainy image and ground truth image captured moments after the rain had
stopped. 275 participants were registered in the challenge and 55 competed in
the final testing phase.

这篇报告回顾了 CVPR 2023 UG2+ 研讨会上单张图像去雨 GT-Rain 挑战的结果，目标是研究真实场景中的雨天现象，提供一个新颖的真实世界雨景图像数据集，并激发创新思路，推动单张图像去雨方法在真实图像上的发展。

GT-Rain 单图像去雨挑战报告

GT-Rain Single Image Deraining Challenge Report

In this paper, we introduce a new concept of incorporating factorized flow
maps as mid-level representations, for bridging the perception and the control
modules in modular learning based robotic frameworks. To investigate the
advantages of factorized flow maps and examine their interplay with the other
types of mid-level representations, we further develop a configurable
framework, along with four different environments that contain both static and
dynamic objects, for analyzing the impacts of factorized optical flow maps on
the performance of deep reinforcement learning agents. Based on this framework,
we report our experimental results on various scenarios, and offer a set of
analyses to justify our hypothesis. Finally, we validate flow factorization in
real world scenarios.

通过在模块化学习的机器人框架中引入因式化流图作为中级表示的新概念，可以极大地促进感知和控制模块之间的关联，并进一步发展一个可配置的框架来分析因式化光流图对深度强化学习智能体性能的影响，在各种场景下验证并根据实验结果提出分析和结论。

探索分解光流作为中层表征

Investigation of Factorized Optical Flows as Mid-Level Representations

Consistently testing autonomous mobile robots in real world scenarios is a
necessary aspect of developing autonomous navigation systems. Each time the
human safety monitor disengages the robot's autonomy system due to the robot
performing an undesirable maneuver, the autonomy developers gain insight into
how to improve the autonomy system. However, we believe that these
disengagements not only show where the system fails, which is useful for
troubleshooting, but also provide a direct learning signal by which the robot
can learn to navigate. We present a reinforcement learning approach for
learning to navigate from disengagements, or LaND. LaND learns a neural network
model that predicts which actions lead to disengagements given the current
sensory observation, and then at test time plans and executes actions that
avoid disengagements. Our results demonstrate LaND can successfully learn to
navigate in diverse, real world sidewalk environments, outperforming both
imitation learning and reinforcement learning approaches. Videos, code, and
other material are available on our website
this https URL

通过观察自主移动机器人在现实世界中的操作失败，提出了一种基于强化学习的导航学习方法，该方法学习一个神经网络模型来避免机器人执行意外操作，并在各种复杂真实的道路环境中表现优异。