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Authors: Mahesh Reginthala 1 ; Yuji Iwahori 2 ; M. K. Bhuyan 1 ; Yoshitsugu Hayashi 2 ; Witsarut Achariyaviriya 2 and Boonserm Kijsirikul 3

Affiliations: 1 Indian Institute of Technology Guwahati, 781039, India ; 2 Chubu University, 487-8501, Japan ; 3 Chulalongkorn University, Bangkok, 20330, Thailand

Keyword(s): Multi-task Learning, Semantic Segmentation, Object Detection, Deep Learning.

Abstract: Lightweight, fast, and accurate deep-learning algorithms are essential for practical deployment in real-world use-cases. Semantic segmentation and object detection are the principal tasks of visual perception. A multi-task network significantly reduces the number of parameters compared to two independent networks running simultaneously for each task. Generally, multi-task networks have shared encoders and multiple independent task-specific decoders. Instead, we modeled our network to exploit the features from both encoder and decoder. We propose the multi-task network that performs both segmentation and detection with only 37.9 million parameters and inference time of 74 milliseconds on a consumer-grade GPU. This network performs two tasks with much fewer parameters and in much less inference time compared to each single task network.

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Paper citation in several formats:
Reginthala, M.; Iwahori, Y.; Bhuyan, M.; Hayashi, Y.; Achariyaviriya, W. and Kijsirikul, B. (2020). Interdependent Multi-task Learning for Simultaneous Segmentation and Detection. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-397-1; ISSN 2184-4313, SciTePress, pages 167-174. DOI: 10.5220/0008949501670174

@conference{icpram20,
author={Mahesh Reginthala. and Yuji Iwahori. and M. K. Bhuyan. and Yoshitsugu Hayashi. and Witsarut Achariyaviriya. and Boonserm Kijsirikul.},
title={Interdependent Multi-task Learning for Simultaneous Segmentation and Detection},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2020},
pages={167-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008949501670174},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Interdependent Multi-task Learning for Simultaneous Segmentation and Detection
SN - 978-989-758-397-1
IS - 2184-4313
AU - Reginthala, M.
AU - Iwahori, Y.
AU - Bhuyan, M.
AU - Hayashi, Y.
AU - Achariyaviriya, W.
AU - Kijsirikul, B.
PY - 2020
SP - 167
EP - 174
DO - 10.5220/0008949501670174
PB - SciTePress

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