English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 Local TagsRelease HistoryDetailsSummary
  Decoupling Zero-Shot Semantic Segmentation

Ding, J., Xue, N., Xia, G.-S., & Dai, D. (2022). Decoupling Zero-Shot Semantic Segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11573-11582). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01129.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/21.11116/0000-000A-16BD-9 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-1272-F
Genre: Conference Paper

Files

show Files
hide Files
:
arXiv:2112.07910.pdf (Preprint), 14MB
 
File Permalink:
-
Name:
arXiv:2112.07910.pdf
Description:
File downloaded from arXiv at 2022-03-09 14:50
OA-Status:
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
:
Ding_Decoupling_Zero-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf (Preprint), 5MB
View   Save
File Permalink:
https://hdl.handle.net/21.11116/0000-000C-1271-0
Name:
Ding_Decoupling_Zero-Shot_Semantic_Segmentation_CVPR_2022_paper.pdf
Description:
-
OA-Status:
Not specified
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
View
Copyright Date:
-
Copyright Info:
These CVPR 2021 papers are the Open Access versions, provided by the Computer Vision Foundation. Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
License:
-

Locators

show

Creators

show
hide
 Creators:
Ding, Jian1, Author
Xue, Nan1, Author
Xia, Gui-Song1, Author
Dai, Dengxin2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              

Content

show
hide
Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Abstract: Zero-shot semantic segmentation (ZS3) aims to segment the novel categories
that have not been seen in the training. Existing works formulate ZS3 as a
pixel-level zero-shot classification problem, and transfer semantic knowledge
from seen classes to unseen ones with the help of language models pre-trained
only with texts. While simple, the pixel-level ZS3 formulation shows the
limited capability to integrate vision-language models that are often
pre-trained with image-text pairs and currently demonstrate great potential for
vision tasks. Inspired by the observation that humans often perform
segment-level semantic labeling, we propose to decouple the ZS3 into two
sub-tasks: 1) a class-agnostic grouping task to group the pixels into segments.
2) a zero-shot classification task on segments. The former sub-task does not
involve category information and can be directly transferred to group pixels
for unseen classes. The latter subtask performs at segment-level and provides a
natural way to leverage large-scale vision-language models pre-trained with
image-text pairs (e.g. CLIP) for ZS3. Based on the decoupling formulation, we
propose a simple and effective zero-shot semantic segmentation model, called
ZegFormer, which outperforms the previous methods on ZS3 standard benchmarks by
large margins, e.g., 35 points on the PASCAL VOC and 3 points on the COCO-Stuff
in terms of mIoU for unseen classes. Code will be released at
https://github.com/dingjiansw101/ZegFormer.

Details

show
hide
Language(s): eng - English
 Dates: 2021-12-1520222022
 Publication Status: Published online
 Pages: 14 pages, 8 figures
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Ding_CVPR2022
DOI: 10.1109/CVPR52688.2022.01129
 Degree: -

Event

show
hide
Title: 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition
Place of Event: New Orleans, LA, USA
Start-/End Date: 2022-06-19 - 2022-06-24

Legal Case

show

Project information

show

Source 1

show
hide
Title: IEEE/CVF Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2022
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Piscataway, NJ : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 11573 - 11582 Identifier: ISBN: 978-1-6654-6946-3