J
Jie Lin
Researcher at Institute for Infocomm Research Singapore
Publications - 98
Citations - 1977
Jie Lin is an academic researcher from Institute for Infocomm Research Singapore. The author has contributed to research in topics: Convolutional neural network & Image retrieval. The author has an hindex of 20, co-authored 94 publications receiving 1398 citations. Previous affiliations of Jie Lin include Beijing Jiaotong University & Agency for Science, Technology and Research.
Papers
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Journal ArticleDOI
Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State
TL;DR: The experimental results show the advantages of the vision-based fatigue detection system for bus driver monitoring on accuracy and robustness for the challenging situations when a camera of an oblique viewing angle to the driver's face is used for driving state monitoring.
Journal ArticleDOI
Overview of the MPEG-CDVS Standard
TL;DR: An overview of the MPEG CDVS standard is given, with emphasis on the development of the core techniques and their technical merits, to push forward the frontiers of compact descriptors in mobile internet industry.
Proceedings ArticleDOI
Object detection meets knowledge graphs
TL;DR: A novel framework of knowledge-aware object detection is proposed, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm, which improves object detection through a re-optimization process to achieve better consistency with background knowledge.
Proceedings ArticleDOI
A*3D Dataset: Towards Autonomous Driving in Challenging Environments
Quang-Hieu Pham,Pierre Sevestre,Ramanpreet Singh Pahwa,Huijing Zhan,Chun Ho Pang,Yuda Chen,Armin Mustafa,Vijay Chandrasekhar,Jie Lin +8 more
TL;DR: A new challenging A*3D dataset which consists of RGB images and LiDAR data with a significant diversity of scene, time, and weather is introduced which addresses the gaps in the existing datasets to push the boundaries of tasks in autonomous driving research to more challenging highly diverse environments.
Journal ArticleDOI
A practical guide to CNNs and Fisher Vectors for image instance retrieval
TL;DR: In this paper, a comprehensive study that systematically evaluates FVs and CNNs for image instance retrieval is presented, which shows that no descriptor is systematically better than the other and that performance gains can usually be obtained by using both types together.