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Authors: Sumanth Chennupati 1 ; Ganesh Sistu 2 ; Senthil Yogamani 2 and Samir Rawashdeh 3

Affiliations: 1 Valeo Troy, U.S.A., University of Michigan-Dearborn and U.S.A. ; 2 Valeo Vision Systems and Ireland ; 3 University of Michigan-Dearborn and U.S.A.

Keyword(s): Semantic Segmentation, Multitask Learning, Auxiliary Tasks, Automated Driving.

Abstract: Decision making in automated driving is highly specific to the environment and thus semantic segmentation plays a key role in recognizing the objects in the environment around the car. Pixel level classification once considered a challenging task which is now becoming mature to be productized in a car. However, semantic annotation is time consuming and quite expensive. Synthetic datasets with domain adaptation techniques have been used to alleviate the lack of large annotated datasets. In this work, we explore an alternate approach of leveraging the annotations of other tasks to improve semantic segmentation. Recently, multi-task learning became a popular paradigm in automated driving which demonstrates joint learning of multiple tasks improves overall performance of each tasks. Motivated by this, we use auxiliary tasks like depth estimation to improve the performance of semantic segmentation task. We propose adaptive task loss weighting techniques to address scale issues in multi-ta sk loss functions which become more crucial in auxiliary tasks. We experimented on automotive datasets including SYNTHIA and KITTI and obtained 3% and 5% improvement in accuracy respectively. (More)

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Paper citation in several formats:
Chennupati, S. ; Sistu, G. ; Yogamani, S. and Rawashdeh, S. (2019). AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 645-652. DOI: 10.5220/0007684106450652

@conference{visapp19,
author={Sumanth Chennupati and Ganesh Sistu and Senthil Yogamani and Samir Rawashdeh},
title={AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={645-652},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007684106450652},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - AuxNet: Auxiliary Tasks Enhanced Semantic Segmentation for Automated Driving
SN - 978-989-758-354-4
IS - 2184-4321
AU - Chennupati, S.
AU - Sistu, G.
AU - Yogamani, S.
AU - Rawashdeh, S.
PY - 2019
SP - 645
EP - 652
DO - 10.5220/0007684106450652
PB - SciTePress

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