TY - GEN
T1 - Multi-task learning with cartesian product-based multi-objective combination for dangerous object detection
AU - Chen, Yaran
AU - Zhao, Dongbin
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Autonomous driving has caused extensively attention of academia and industry. Vision-based dangerous object detection is a crucial technology of autonomous driving which detects object and assesses its danger with distance to warn drivers. Previous vision-based dangerous object detections apply two independent models to deal with object detection and distance prediction, respectively. In this paper, we show that object detection and distance prediction have visual relationship, and they can be improved by exploiting the relationship. We jointly optimize object detection and distance prediction with a novel multi-task learning (MTL) model for using the relationship. In contrast to traditional MTL which uses linear multi-task combination strategy, we propose a Cartesian product-based multi-target combination strategy for MTL to consider the dependent among tasks. The proposed novel MTL method outperforms than the traditional MTL and single task methods by a series of experiments.
AB - Autonomous driving has caused extensively attention of academia and industry. Vision-based dangerous object detection is a crucial technology of autonomous driving which detects object and assesses its danger with distance to warn drivers. Previous vision-based dangerous object detections apply two independent models to deal with object detection and distance prediction, respectively. In this paper, we show that object detection and distance prediction have visual relationship, and they can be improved by exploiting the relationship. We jointly optimize object detection and distance prediction with a novel multi-task learning (MTL) model for using the relationship. In contrast to traditional MTL which uses linear multi-task combination strategy, we propose a Cartesian product-based multi-target combination strategy for MTL to consider the dependent among tasks. The proposed novel MTL method outperforms than the traditional MTL and single task methods by a series of experiments.
KW - Dangerous object detection
KW - Multi-task learning and convolutional neural network
UR - http://www.scopus.com/inward/record.url?scp=85021690124&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-59072-1_4
DO - 10.1007/978-3-319-59072-1_4
M3 - Conference Proceeding
AN - SCOPUS:85021690124
SN - 9783319590714
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 28
EP - 35
BT - Advances in Neural Networks - ISNN 2017 - 14th International Symposium, ISNN 2017, Proceedings
A2 - Leung, Andrew
A2 - Cong, Fengyu
A2 - Wei, Qinglai
PB - Springer Verlag
T2 - 14th International Symposium on Neural Networks, ISNN 2017
Y2 - 21 June 2017 through 26 June 2017
ER -