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2017 DAVIS Challenge and Dataset

Post · Apr 4, 2017 18:41 ·

davis-challange video object-segmentation

Ooh, this is interesting. There’s a new DAVIS Challenge for 2017. It comes with a beautiful updated dataset composed of some new videos and some old videos that were relabeled with multiple objects. There’s been a lot of research in the area of semi-supervised video object detection lately, so we expect there will be some strong competitors. Especially since the results of the public challenge will be presented during a workshop at CVPR 2017 in Hawaii.

Highlights From the Paper

  • “The new dataset consists of 150 sequences, totaling 10459 annotated frames and 376 objects.”
  • “The algorithm is given a video sequence and the mask of the objects in the first frame, and the output should be the masks of those objects in the rest of the frames.”

Arxiv Abstract

  • Jordi Pont-Tuset
  • Federico Perazzi
  • Sergi Caelles
  • Pablo Arbeláez
  • Alex Sorkine-Hornung
  • Luc Van Gool

We present the 2017 DAVIS Challenge, a public 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 and define the evaluation metrics of the competition.

Read the paper (pdf) »