Distractor-Aware Video Object Segmentation

Andreas Robinson*, Abdelrahman Eldesokey, Michael Felsberg

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently. We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn [3] method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS 2017 validation dataset, and improves over the baseline on the DAVIS 2017 test-dev benchmark by 4.6% points.

Original languageEnglish (US)
Title of host publicationPattern Recognition - 43rd DAGM German Conference, DAGM GCPR 2021, Proceedings
EditorsChristian Bauckhage, Juergen Gall, Alexander Schwing
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030926588
StatePublished - 2021
Event43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021 - Virtual, Online
Duration: Sep 28 2021Oct 1 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13024 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference43rd DAGM German Conference on Pattern Recognition, DAGM GCPR 2021
CityVirtual, Online

Bibliographical note

Funding Information:
Acknowledgements. This project was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation, the Excellence Center at Linköping-Lund in Information Technology (ELLIIT), the Swedish Research Council grant no. 2018-04673, and the Swedish Foundation for Strategic Research (SSF) project Symbicloud.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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