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Kunming Li
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2020 – today
- 2024
- [i5]Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, You-Fu Li, He Kong:
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction. CoRR abs/2401.04872 (2024) - 2023
- [c14]Kunming Li, Yijun Chen, Mao Shan, Jiachen Li, Stewart Worrall, Eduardo M. Nebot:
Game Theory-Based Simultaneous Prediction and Planning for Autonomous Vehicle Navigation in Crowded Environments. ITSC 2023: 2977-2984 - [c13]Yu Liu, Yuexin Zhang, Kunming Li, Yongliang Qiao, Stewart Worrall, Youfu Li, He Kong:
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction. ITSC 2023: 4360-4366 - 2022
- [c12]Kunming Li, Mao Shan, Stewart Worrall, Eduardo M. Nebot:
Crowd Prediction and Autonomous Navigation with Partial Observations. ITSC 2022: 1829-1836 - [c11]Kunming Li, Mao Shan, Stuart Eiffert, Stewart Worrall, Eduardo M. Nebot:
Towards Collision-Free Probabilistic Pedestrian Motion Prediction for Autonomous Vehicles. IV 2022: 1114-1120 - 2021
- [c10]Kunming Li, Stuart Eiffert, Mao Shan, Francisco Gomez-Donoso, Stewart Worrall, Eduardo M. Nebot:
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases. ICRA 2021: 14241-14247 - 2020
- [j2]Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall, Salah Sukkarieh, Eduardo M. Nebot:
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction Using a Graph Vehicle-Pedestrian Attention Network. IEEE Robotics Autom. Lett. 5(4): 5026-5033 (2020) - [j1]Mingqian Liu, Kunming Li, Hao Song, Yunfei Chen, Xiuhui Gao, Fengkui Gong:
Using Heterogeneous Satellites for Passive Detection of Moving Aerial Target. Remote. Sens. 12(7): 1150 (2020) - [c9]Kunming Li, Mao Shan, Karan Narula, Stewart Worrall, Eduardo M. Nebot:
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles. ITSC 2020: 1-8 - [i4]Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall, Salah Sukkarieh, Eduardo M. Nebot:
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network. CoRR abs/2006.12906 (2020) - [i3]Kunming Li, Stuart Eiffert, Mao Shan, Francisco Gomez-Donoso, Stewart Worrall, Eduardo M. Nebot:
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases. CoRR abs/2011.11190 (2020) - [i2]Kunming Li, Mao Shan, Karan Narula, Stewart Worrall, Eduardo M. Nebot:
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles. CoRR abs/2011.11191 (2020)
2010 – 2019
- 2019
- [c8]Kunming Li, Yijun Gu, Peijing Zhang, An Wang, Wenzheng Li:
Research on KNN Algorithm in Malicious PDF Files Classification under Adversarial Environment. ICBDC 2019: 156-159 - [c7]Zhixiang Hao, Shaodi You, Yu Li, Kunming Li, Feng Lu:
Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal. ICCV Workshops 2019: 4340-4349 - [c6]Zhonghui You, Jinmian Ye, Kunming Li, Zenglin Xu, Ping Wang:
Adversarial Noise Layer: Regularize Neural Network by Adding Noise. ICIP 2019: 909-913 - [c5]Wenzheng Li, Yijun Gu, An Wang, Kunming Li:
Improvement of Ego Network Detection Algorithm Based on Cluster Validity Index. ISI 2019: 35-40 - 2018
- [c4]Weixuan Sun, Shaodi You, Janine Walker, Kunming Li, Nick Barnes:
Structural Edge Detection: A Dataset and Benchmark. DICTA 2018: 1-8 - [i1]Zhonghui You, Jinmian Ye, Kunming Li, Ping Wang:
Adversarial Noise Layer: Regularize Neural Network By Adding Noise. CoRR abs/1805.08000 (2018) - 2017
- [c3]Kunming Li, Yu Li, Shaodi You, Nick Barnes:
Photo-Realistic Simulation of Road Scene for Data-Driven Methods in Bad Weather. ICCV Workshops 2017: 491-500 - 2011
- [c2]Kunming Li, Jianbao Chen:
A Study of the Nonlinear Relation between CPI and International Oil Price Based on STR Model. CSEE (4) 2011: 324-329
2000 – 2009
- 2007
- [c1]Yun Yao Lee, Chien-Yu Chi, Kunming Li:
Nonlinear Element Compensation by Gain Adjustment and Dither Smoothing. ISIC 2007: 487-492
Coauthor Index
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last updated on 2024-10-31 20:19 CET by the dblp team
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