We present the 2019 DAVIS Challenge on Video Object Segmentation, the third
edition of the DAVIS Challenge series, a public competition designed for the
task of Video Object Segmentation (VOS). In addition to the original
semi-supervised track and the interactive track introduced in the previous
edition, a new unsupervised multi-object track will be featured this year. In
the newly introduced track, participants are asked to provide non-overlapping
object proposals on each image, along with an identifier linking them between
frames (i.e. video object proposals), without any test-time human supervision
(no scribbles or masks provided on the test video). In order to do so, we have
re-annotated the train and val sets of DAVIS 2017 in a concise way that
facilitates the unsupervised track, and created new test-dev and test-challenge
sets for the competition. Definitions, rules, and evaluation metrics for the
unsupervised track are described in detail in this paper.

本文介绍了 2019 年 DAVIS 挑战赛关于视频对象分割的任务，其中包括原始半监督轨迹、交互轨迹，以及新的无人监督多对象轨迹。同时，为了促进无人监督的轨迹，DAVIS 2017 训练、验证集重新标注，并为比赛创建了新的测试集，无人监督轨迹的定义、规则和评估指标在本文中详细描述。

2019 年 DAVIS VOS 竞赛：无监督多对象分割

The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation

We present the 2018 DAVIS Challenge on Video Object Segmentation, a public
competition specifically designed for the task of video object segmentation. It
builds upon the DAVIS 2017 dataset, which was presented in the previous edition
of the DAVIS Challenge, and added 100 videos with multiple objects per sequence
to the original DAVIS 2016 dataset. Motivated by the analysis of the results of
the 2017 edition, the main track of the competition will be the same than in
the previous edition (segmentation given the full mask of the objects in the
first frame -- semi-supervised scenario). This edition, however, also adds an
interactive segmentation teaser track, where the participants will interact
with a web service simulating the input of a human that provides scribbles to
iteratively improve the result.

2018 年的 DAVIS 挑战赛主要包括视频对象分割的任务，比赛使用 DAVIS 2017 数据集，增加了 100 个多对象序列视频，添加了交互式分割 Teaser 赛道。

2018 年 DAVIS 视频目标分割挑战赛

The 2018 DAVIS Challenge on Video Object Segmentation

Conventional video segmentation methods often rely on temporal continuity to
propagate masks. Such an assumption suffers from issues like drifting and
inability to handle large displacement. To overcome these issues, we formulate
an effective mechanism to prevent the target from being lost via adaptive
object re-identification. Specifically, our Video Object Segmentation with
Re-identification (VS-ReID) model includes a mask propagation module and a ReID
module. The former module produces an initial probability map by flow warping
while the latter module retrieves missing instances by adaptive matching. With
these two modules iteratively applied, our VS-ReID records a global mean
(Region Jaccard and Boundary F measure) of 0.699, the best performance in 2017
DAVIS Challenge.

本文提出了一种基于视频对象重识别的视频分割方法，通过使用自适应的目标重识别机制来避免传统方法中的漂移问题和无法处理大位移的弊端，该方法在 2017 年 DAVIS Challenge 中取得了最佳性能。

基于重识别的视频目标分割

Video Object Segmentation with Re-identification

We present the 2017 DAVIS Challenge on Video Object Segmentation, a public
dataset, benchmark, and competition specifically designed for the task of video
object segmentation. Following the footsteps of other successful initiatives,
such as ILSVRC and PASCAL VOC, which established the avenue of research in the
fields of scene classification and semantic segmentation, the DAVIS Challenge
comprises a dataset, an evaluation methodology, and a public competition with a
dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on
the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which
has fostered the development of several novel state-of-the-art video object
segmentation techniques. In this paper we describe the scope of the benchmark,
highlight the main characteristics of the dataset, define the evaluation
metrics of the competition, and present a detailed analysis of the results of
the participants to the challenge.

本文介绍了 2017 年 DAVIS 视频物体分割挑战赛，该挑战赛是一个公共数据集，评测方法和比赛，它是为了特别针对视频物体分割任务而设计的，挑战赛遵循其他成功首创的做法，如 ILSVRC 和 PASCAL VOC，DAVIS 挑战赛包括数据集、评估方法和与 CVPR 2017 同地举办的独立工作坊，本文还描述了该基准测试的范围、数据集的主要特征、竞赛的评价指标，并对参赛者的结果进行了详细分析。