Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation

Zhengyang Lyu; Pierre Beauseroy; Alexandre Baussard

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Paper

Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation

Topics: Advanced Learning Methods; Deep Learning and Neural Networks; Machine Learning Methods

In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 402-409, 2024 , Rome, Italy

Authors: Zhengyang Lyu ; Pierre Beauseroy and Alexandre Baussard

Affiliation: Université de Technologie de Troyes, LIST3N, 12 Rue Marie Curie, 10300 Troyes, France

Keyword(s): Semantic Segmentation, Weak Supervision, Convolutional Neural Network, Support Vector Machine.

Abstract: In this paper, we propose a study of an expansion method based on an image-specific classifier and multi-features for Weakly Supervised Semantic Segmentation (WSSS) with only image-level labels. Recent WSSS methods focus mainly on enhancing the pseudo masks to improve the segmentation performance by obtaining improved Class Activation Maps (CAM) or by applying post-process methods that combine expansion and refinement. Most of these methods either lack of consideration for the balance between resolution and semantics in the used features, or are carried out globally for the whole data set, without taking into account potential additional improvements based on the specific content of the image. Previously, we proposed an image-specific expansion method using multi-features to alleviate these limitations. This new study aims firstly at determining the upper performance limit of the proposed method using the ground truth masks, and secondly at analysing this performance limit in relatio n with the features chosen. Experiments show that our expansion method can achieve promising results, when used with the ground truth (upper performance) and the features that strike a balance between semantics and resolution. (More)

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Paper citation in several formats:

Lyu, Z.; Beauseroy, P. and Baussard, A. (2024). Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 402-409. DOI: 10.5220/0012462100003654

@conference{icpram24,
author={Zhengyang Lyu. and Pierre Beauseroy. and Alexandre Baussard.},
title={Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={402-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012462100003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Study of an Expansion Method Based on an Image-Specific Classifier and Multi-Features for Weakly Supervised Semantic Segmentation
SN - 978-989-758-684-2
IS - 2184-4313
AU - Lyu, Z.
AU - Beauseroy, P.
AU - Baussard, A.
PY - 2024
SP - 402
EP - 409
DO - 10.5220/0012462100003654
PB - SciTePress