loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Yuwen Heng ; Yihong Wu ; Srinandan Dasmahapatra and Hansung Kim

Affiliation: Vision, Learning and Control Research Group (VLC), School of Electronics and Computer Science (ECS), University of Southampton, U.K.

Keyword(s): Dense Material Segmentation, Material Recognition, Deep Learning, Scene Understanding, Image Segmentation.

Abstract: Contextual information reduces the uncertainty in the dense material segmentation task to improve segmentation quality. Typical contextual information includes object, place labels or extracted feature maps by a neural network. Existing methods typically adopt a pre-trained network to generate contextual feature maps without fine-tuning since dedicated material datasets do not contain contextual labels. As a consequence, these contextual features may not improve the material segmentation performance. In consideration of this problem, this paper proposes a hybrid network architecture, the CAM-SegNet, to learn from contextual and material features during training jointly without extra contextual labels. The utility of our CAM-SegNet is demonstrated by guiding the network to learn boundary-related contextual features with the help of a self-training approach. Experiments show that CAM-SegNet can recognise materials that have similar appearances, achieving an improvement of 3-20% on accu racy and 6-28% on Mean IoU. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.116.42.208

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Heng, Y.; Wu, Y.; Dasmahapatra, S. and Kim, H. (2022). CAM-SegNet: A Context-Aware Dense Material Segmentation Network for Sparsely Labelled Datasets. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP; ISBN 978-989-758-555-5; ISSN 2184-4321, SciTePress, pages 190-201. DOI: 10.5220/0010853200003124

@conference{visapp22,
author={Yuwen Heng. and Yihong Wu. and Srinandan Dasmahapatra. and Hansung Kim.},
title={CAM-SegNet: A Context-Aware Dense Material Segmentation Network for Sparsely Labelled Datasets},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP},
year={2022},
pages={190-201},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010853200003124},
isbn={978-989-758-555-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 5: VISAPP
TI - CAM-SegNet: A Context-Aware Dense Material Segmentation Network for Sparsely Labelled Datasets
SN - 978-989-758-555-5
IS - 2184-4321
AU - Heng, Y.
AU - Wu, Y.
AU - Dasmahapatra, S.
AU - Kim, H.
PY - 2022
SP - 190
EP - 201
DO - 10.5220/0010853200003124
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic