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Published in ACM International Conference on Multimedia, 2020
In this work, we present a novel hybrid dynAmic-static Context-aware attenTION NETwork (ACTION-NET) for action assessment in long videos
Recommended citation: Ling-An Zeng, Fa-Ting Hong, Wei-Shi Zheng, Qi-Zhi Yu, Wei Zeng, Yao-Wei Wang, and Jian-Huang Lai. Hybrid Dynamic-static Context-aware Attention Network for Action Assessment in Long Videos. Proc. of ACM International Conference on Multimedia (ACM MM), 2020.
Published in European Conference on Computer Vision, 2020
We address the weakly supervised video highlight detectionproblem for learning to detect the segments that are more attractivein training videos given their video event label but without expensivesupervision of manually annotating highlight segments.
Recommended citation: Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li, and Wei-Shi Zheng. MINI-Net: Multiple Instance Ranking Networkfor Video Highlight Detection. In European Conference on Computer Vision (ECCV), 2020.
Published in International Conference on Computer Vision and Pattern Recognition, 2019
In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning.
Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection
Published in International Conference on Computer Vision and Pattern Recognition, 2020
In this work, we study semi-supervised learning in the context of important people detection and propose a semi-supervised learning method for this task.
Recommended citation: Fa-Ting Hong, Wei-Hong Li and Wei-Shi Zheng. Learning to Detect Important People in Unlabelled Images for Semi-supervised Important People Detection. In Computer Vision and Pattern Recognition, 2020.
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Published in 2017 IEEE International Conference on Real-time Computing and Robotics, 2017
The paper introduces a method which is based o n genetic algorithm to select more properly parameters for local path planning of mobile robot.
Recommended citation: Liang, Y., Hong, F., Lin, Q., Bi, S., & Feng, L. (2017, July). Optimization of robot path planning parameters based on genetic algorithm. In 2017 IEEE International Conference on Real-time Computing and Robotics (RCAR) (pp. 529-534). IEEE.