Interdependent Multi-task Learning for Simultaneous Segmentation and Detection

Mahesh Reginthala, Yuji Iwahori, M. Bhuyan, Yoshitsugu Hayashi, Witsarut Achariyaviriya, Boonserm Kijsirikul

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 Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 167-174. DOI: 10.5220/0008949501670174


in Bibtex Style

@conference{icpram20,
author={Mahesh Reginthala and Yuji Iwahori and M. 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 - Volume 1: ICPRAM,},
year={2020},
pages={167-174},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008949501670174},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Interdependent Multi-task Learning for Simultaneous Segmentation and Detection
SN - 978-989-758-397-1
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