Image restoration aims to reconstruct degraded images, e.g., denoising or
deblurring. Existing works focus on designing task-specific methods and there
are inadequate attempts at universal methods. However, simply unifying multiple
tasks into one universal architecture suffers from uncontrollable and undesired
predictions. To address those issues, we explore prompt learning in universal
architectures for image restoration tasks. In this paper, we present
Degradation-aware Visual Prompts, which encode various types of image
degradation, e.g., noise and blur, into unified visual prompts. These
degradation-aware prompts provide control over image processing and allow
weighted combinations for customized image restoration. We then leverage
degradation-aware visual prompts to establish a controllable and universal
model for image restoration, called ProRes, which is applicable to an extensive
range of image restoration tasks. ProRes leverages the vanilla Vision
Transformer (ViT) without any task-specific designs. Furthermore, the
pre-trained ProRes can easily adapt to new tasks through efficient prompt
tuning with only a few images. Without bells and whistles, ProRes achieves
competitive performance compared to task-specific methods and experiments can
demonstrate its ability for controllable restoration and adaptation for new
tasks. The code and models will be released in
https://github.com/leonmakise/ProRes.

本文提出了一种利用降噪、去模糊等各种图像退化类型的可控视觉提示符来实现图像恢复的通用模型 ProRes，该模型可以简单调节以适应新任务，并且在控制和自适应性能方面比特定任务的方法更具竞争力。

ProRes：探索对降级感知的视觉提示来进行通用图像恢复

ProRes: Exploring Degradation-aware Visual Prompt for Universal Image  Restoration

We find Mask2Former also achieves state-of-the-art performance on video
instance segmentation without modifying the architecture, the loss or even the
training pipeline. In this report, we show universal image segmentation
architectures trivially generalize to video segmentation by directly predicting
3D segmentation volumes. Specifically, Mask2Former sets a new state-of-the-art
of 60.4 AP on YouTubeVIS-2019 and 52.6 AP on YouTubeVIS-2021. We believe
Mask2Former is also capable of handling video semantic and panoptic
segmentation, given its versatility in image segmentation. We hope this will
make state-of-the-art video segmentation research more accessible and bring
more attention to designing universal image and video segmentation
architectures.

通过直接预测 3D 分割体积，我们展示了通用的图像分割架构轻松推广到视频分割领域，Mask2Former 在视频实例分割方面也取得了最先进的性能，同时在 YouTubeVIS-2019 和 YouTubeVIS-2021 上分别达到了 60.4 AP 和 52.6 AP。由于在图像分割方面的多才多艺，我们相信 Mask2Former 也能处理视频语义和全景分割，我们希望这将使最新的视频分割研究更易于使用并引起更多人对通用图像和视频分割架构的关注。