VOSTR: Video Object Segmentation via Transferable Representations
VOSTR: Video Object Segmentation via Transferable Representations
About this item
Full title
Author / Creator
Publisher
New York: Springer US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Springer US
Subjects
More information
Scope and Contents
Contents
In order to learn video object segmentation models, conventional methods require a large amount of pixel-wise ground truth annotations. However, collecting such supervised data is time-consuming and labor-intensive. In this paper, we exploit existing annotations in source images and transfer such visual information to segment videos with unseen obj...
Alternative Titles
Full title
VOSTR: Video Object Segmentation via Transferable Representations
Authors, Artists and Contributors
Author / Creator
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2388669553
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2388669553
Other Identifiers
ISSN
0920-5691
E-ISSN
1573-1405
DOI
10.1007/s11263-019-01224-x
