# The 2017 DAVIS Challenge on Video Object Segmentation

Source: [The 2017 DAVIS Challenge on Video Object Segmentation](<https://www.sota2.com/research/paper/68fa640e1e209131cea02695>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:55:10.976Z

Authors: Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbel\\'aez, Alex Sorkine-Hornung, Luc Van Gool

Published: 2017-04-03T16:44:46.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1704.00675>)

[PDF](<https://arxiv.org/pdf/1704.00675.pdf>)