From pixels to regions: Toward universal image segmentation
Cheng, Bowen
Loading…
Permalink
https://hdl.handle.net/2142/116182
Description
Title
From pixels to regions: Toward universal image segmentation
Author(s)
Cheng, Bowen
Issue Date
2022-07-06
Director of Research (if dissertation) or Advisor (if thesis)
Schwing, Alexander
Doctoral Committee Chair(s)
Schwing, Alexander
Committee Member(s)
Shi, Humphrey
Hasegawa-Johnson, Mark
Darrell, Trevor
Liang, Zhi-Pei
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
computer vision
image segmentation
semantic segmentation
instance segmentation
panoptic segmentation
Language
eng
Abstract
Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task: semantic segmentation is usually formulated as per-pixel classification and mask classification dominates instance-level segmentation tasks. In this dissertation, we demonstrate how to build a single unified architecture that can address any image segmentation task. We first introduce an effort in unifying image segmentation with either per-pixel classification (Panoptic-DeepLab) or mask classification (MaskFormer). We observe mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks. Based on this observation we propose Mask2Former, which outperforms even the best specialized architectures by a significant margin on four popular datasets for three image segmentation tasks (panoptic, instance and semantic). Then we discuss how to evaluate image segmentation models with a new Boundary IoU metric. Finally, we conclude this dissertation with promising future directions to explore.
Use this login method if you
don't
have an
@illinois.edu
email address.
(Oops, I do have one)
IDEALS migrated to a new platform on June 23, 2022. If you created
your account prior to this date, you will have to reset your password
using the forgot-password link below.
