Although recent inpainting approaches have demonstrated significant
improvements with deep neural networks, they still suffer from artifacts such
as blunt structures and abrupt colors when filling in the missing regions. To
address these issues, we propose an external-internal inpainting scheme with a
monochromic bottleneck that helps image inpainting models remove these
artifacts. In the external learning stage, we reconstruct missing structures
and details in the monochromic space to reduce the learning dimension. In the
internal learning stage, we propose a novel internal color propagation method
with progressive learning strategies for consistent color restoration.
Extensive experiments demonstrate that our proposed scheme helps image
inpainting models produce more structure-preserved and visually compelling
results.

本研究提出了一种基于外部 - 内部的修复方案，利用单色瓶颈以及逐步学习策略实现对缺失区域颜色的一致性修复，实验结果表明该方案有助于实现保留更多结构和视觉效果更佳的图像修复。

外内学习和单色瓶颈的图像修复

Image Inpainting with External-internal Learning and Monochromic  Bottleneck

Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.

本研究提出了一框架，结合了本地和全局策略，来利用深度学习技术实现视频中颜色的传播，使得颜色的传播更加稳定，并且利用了语义信息的特点，具有很高的优越性。

深度视频颜色传播

Deep Video Color Propagation

We propose a technique that propagates information forward through video
data. The method is conceptually simple and can be applied to tasks that
require the propagation of structured information, such as semantic labels,
based on video content. We propose a 'Video Propagation Network' that processes
video frames in an adaptive manner. The model is applied online: it propagates
information forward without the need to access future frames. In particular we
combine two components, a temporal bilateral network for dense and video
adaptive filtering, followed by a spatial network to refine features and
increased flexibility. We present experiments on video object segmentation and
semantic video segmentation and show increased performance comparing to the
best previous task-specific methods, while having favorable runtime.
Additionally we demonstrate our approach on an example regression task of color
propagation in a grayscale video.

该研究提出了使用视频传播网络进行语义分割的方法，该方法使用时不需要访问未来帧，并且与基于任务的最佳方法相比具有更高的性能。