We present a new flow-based video completion algorithm. Previous flow
completion methods are often unable to retain the sharpness of motion
boundaries. Our method first extracts and completes motion edges, and then uses
them to guide piecewise-smooth flow completion with sharp edges. Existing
methods propagate colors among local flow connections between adjacent frames.
However, not all missing regions in a video can be reached in this way because
the motion boundaries form impenetrable barriers. Our method alleviates this
problem by introducing non-local flow connections to temporally distant frames,
enabling propagating video content over motion boundaries. We validate our
approach on the DAVIS dataset. Both visual and quantitative results show that
our method compares favorably against the state-of-the-art algorithms.

本文介绍了一种新的基于流的视频补全算法，采用提取与补全运动边缘，引入非局部流连接到时间上远离的帧中，来解决运动边界障碍问题，通过在 DAVIS 数据集上的实验证明其优于现有算法。

流边引导的视频补全

Flow-edge Guided Video Completion

We propose a method for unsupervised video object segmentation by
transferring the knowledge encapsulated in image-based instance embedding
networks. The instance embedding network produces an embedding vector for each
pixel that enables identifying all pixels belonging to the same object. Though
trained on static images, the instance embeddings are stable over consecutive
video frames, which allows us to link objects together over time. Thus, we
adapt the instance networks trained on static images to video object
segmentation and incorporate the embeddings with objectness and optical flow
features, without model retraining or online fine-tuning. The proposed method
outperforms state-of-the-art unsupervised segmentation methods in the DAVIS
dataset and the FBMS dataset.

提出一种用于无监督视频目标分割的方法，通过转移图像实例嵌入网络中封装的知识。通过将实例嵌入与物体性和光流特征相结合，无需模型重新训练或在线微调，即可将物体链接起来。该方法在 DAVIS 数据集和 FBMS 数据集上优于最先进的无监督分割方法。