loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Sijie Hu ; Fabien Bonardi ; Samia Bouchafa and Désiré Sidibé

Affiliation: University Paris-Saclay, Univ. Evry, IBISC, 91020, Evry, France

Keyword(s): Multi-branch, Encoder-decoder, Complementary Learning, Supervised Learning, Semantic Segmentation.

Abstract: Recently, many methods with complex structures were proposed to address image parsing tasks such as image segmentation. These well-designed structures are hardly to be used flexibly and require a heavy footprint. This paper focuses on a popular semantic segmentation framework known as encoder-decoder, and points out a phenomenon that existing decoders do not fully integrate the information extracted by the encoder. To alleviate this issue, we propose a more general two-branch paradigm, composed of a main branch and an auxiliary branch, without increasing the number of parameters, and a boundary enhanced loss computation strategy to make two-branch decoders learn complementary information adaptively instead of explicitly indicating the specific learning element. In addition, one branch learn pixels that are difficult to resolve in another branch making a competition between them, which promotes the model to learn more efficiently. We evaluate our approach on two challenging image segm entation datasets and show its superior performance in different baseline models. We also perform an ablation study to tease apart the effects of different settings. Finally, we show our two-branch paradigm can achieve satisfactory results when remove the auxiliary branch in the inference stage, so that it can be applied to low-resource systems. (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 44.206.227.65

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:
Hu, S.; Bonardi, F.; Bouchafa, S. and Sidibé, D. (2022). A General Two-branch Decoder Architecture for Improving Encoder-decoder Image Segmentation Models. 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 374-381. DOI: 10.5220/0010818800003124

@conference{visapp22,
author={Sijie Hu. and Fabien Bonardi. and Samia Bouchafa. and Désiré Sidibé.},
title={A General Two-branch Decoder Architecture for Improving Encoder-decoder Image Segmentation Models},
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={374-381},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010818800003124},
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 - A General Two-branch Decoder Architecture for Improving Encoder-decoder Image Segmentation Models
SN - 978-989-758-555-5
IS - 2184-4321
AU - Hu, S.
AU - Bonardi, F.
AU - Bouchafa, S.
AU - Sidibé, D.
PY - 2022
SP - 374
EP - 381
DO - 10.5220/0010818800003124
PB - SciTePress