SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-Task Learning

Chengzhi Wu, Linxi Qiu, Kanran Zhou, Julius Pfrommer, Julius Pfrommer, Jürgen Beyerer

2023

Abstract

In this paper, we develop a novel benchmark suite including both a 2D synthetic image dataset and a 3D synthetic point cloud dataset. Our work is a sub-task in the framework of a remanufacturing project, in which small electric motors are used as fundamental objects. Apart from the given detection, classification, and segmentation annotations, the key objects also have multiple learnable attributes with ground truth provided. This benchmark can be used for computer vision tasks including 2D/3D detection, classification, segmentation, and multi-attribute learning. It is worth mentioning that most attributes of the motors are quantified as continuously variable rather than binary, which makes our benchmark well-suited for the less explored regression tasks. In addition, appropriate evaluation metrics are adopted or developed for each task and promising baseline results are provided. We hope this benchmark can stimulate more research efforts on the sub-domain of object attribute learning and multi-task learning in the future.

Paper Citation

in Harvard Style

Wu C., Qiu L., Zhou K., Pfrommer J. and Beyerer J. (2023). SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-Task Learning. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 529-540. DOI: 10.5220/0011718400003417

in Bibtex Style

@conference{visapp23,
author={Chengzhi Wu and Linxi Qiu and Kanran Zhou and Julius Pfrommer and Jürgen Beyerer},
title={SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-Task Learning},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={529-540},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011718400003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - SynMotor: A Benchmark Suite for Object Attribute Regression and Multi-Task Learning
SN - 978-989-758-634-7
AU - Wu C.
AU - Qiu L.
AU - Zhou K.
AU - Pfrommer J.
AU - Beyerer J.
PY - 2023
SP - 529
EP - 540
DO - 10.5220/0011718400003417
PB - SciTePress