Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2021

Abstract

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultrahigh resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware contextual correlation based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present a contextual semantics refinement network that associates the local segmentation result with its contextual semantics, and thus is endowed with the ability of reducing boundary artifacts and refining mask contours during the generation of final high-resolution mask. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks.

Keywords

Contextual semantics, High resolution, High resolution image segmentation, Locality aware, Realistic applications, Resolution images, Segmentation models, Segmentation results, Semantic refinement, Ultra high resolution

Discipline

Databases and Information Systems

Research Areas

Information Systems and Management

Publication

Proceedings of the 18th IEEE/CVF International Conference on Computer Vision, Virtual, Online, 2021 October 11-17

First Page

7232

Last Page

7241

ISBN

9781665428125

Identifier

10.1109/ICCV48922.2021.00716

Publisher

IEEE

City or Country

New Jersey

Additional URL

https://doi.org/10.1109/ICCV48922.2021.00716

Download

Share

COinS