Title: The 2017 DAVIS Challenge on Video Object Segmentation,
Abstract: 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.