Multi-task learning with Cartesianproduct-based multi-objective combination fordangerous object detection
Yaran Chen1,2; Dongbin Zhao1,2
2017
会议录名称Part of the Lecture Notes in Computer Science book series (LNCS, volume 10261)
期号*
页码28–35
摘要
       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. 
 
Multi-task Learning with Cartesian Product-Based Multi-objective Combination for Dangerous Object Detection. Available from: https://www.researchgate.net/publication/318136674_Multi-task_Learning_with_Cartesian_Product-Based_Multi-objective_Combination_for_Dangerous_Object_Detection [accessed Dec 31 2017].
关键词Dangerous Object Detection Multi-task Learning Convolutional Neural Network
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/19421
专题复杂系统管理与控制国家重点实验室_深度强化学习
作者单位1.The State Key Laboratory of Management and Control for Complex Systems, Institute of AutomationChinese Academy of SciencesBeijingChina
2.The University of Chinese Academy of SciencesBeijingChina
推荐引用方式
GB/T 7714
Yaran Chen,Dongbin Zhao. Multi-task learning with Cartesianproduct-based multi-objective combination fordangerous object detection[C],2017:28–35.
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