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Rongrong Ni
Rongrong Ni
在 cczu.edu.cn 的电子邮件经过验证
标题
引用次数
引用次数
年份
Facial expression recognition using weighted mixture deep neural network based on double-channel facial images
B Yang, J Cao, R Ni, Y Zhang
IEEE access 6, 4630-4640, 2017
2442017
Real-time human motion capture based on wearable inertial sensor networks
J Li, X Liu, Z Wang, H Zhao, T Zhang, S Qiu, X Zhou, H Cai, R Ni, ...
IEEE Internet of Things Journal 9 (11), 8953-8966, 2021
442021
Anomaly detection in moving crowds through spatiotemporal autoencoding and additional attention
B Yang, J Cao, R Ni, L Zou
Advances in Multimedia 2018 (1), 2087574, 2018
302018
Continual learning-based trajectory prediction with memory augmented networks
B Yang, F Fan, R Ni, J Li, L Kiong, X Liu
Knowledge-Based Systems 258, 110022, 2022
282022
Facial expression recognition through cross-modality attention fusion
R Ni, B Yang, X Zhou, A Cangelosi, X Liu
IEEE Transactions on Cognitive and Developmental Systems 15 (1), 175-185, 2022
262022
Real-time hand gesture tracking for human–computer interface based on multi-sensor data fusion
J Li, X Liu, Z Wang, T Zhang, S Qiu, H Zhao, X Zhou, H Cai, R Ni, ...
IEEE Sensors Journal 21 (23), 26642-26654, 2021
252021
A multi-task learning network with a collision-aware graph transformer for traffic-agents trajectory prediction
B Yang, F Fan, R Ni, H Wang, A Jafaripournimchahi, H Hu
IEEE Transactions on Intelligent Transportation Systems, 2024
162024
Multi-granularity scenarios understanding network for trajectory prediction
B Yang, J Yang, R Ni, C Yang, X Liu
Complex & Intelligent Systems 9 (1), 851-864, 2023
142023
Meta-IRLSOT++: A meta-inverse reinforcement learning method for fast adaptation of trajectory prediction networks
B Yang, Y Lu, R Wan, H Hu, C Yang, R Ni
Expert Systems with Applications 240, 122499, 2024
122024
Emotion recognition through combining EEG and EOG over relevant channels with optimal windowing
H Cai, X Liu, R Ni, S Song, A Cangelosi
IEEE Transactions on Human-Machine Systems 53 (4), 697-706, 2023
102023
FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene
B Yang, S Yang, P Wang, H Wang, J Jiang, R Ni, C Yang
Computers and Electronics in Agriculture 217, 108623, 2024
92024
Pedestrians crossing intention anticipation based on dual‐channel action recognition and hierarchical environmental context
R Ni, B Yang, Z Wei, H Hu, C Yang
IET Intelligent Transport Systems 17 (2), 255-269, 2023
92023
Combination of EOG and EEG for emotion recognition over different window sizes
H Cai, X Liu, A Jiang, R Ni, X Zhou, A Cangelosi
2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS), 1-6, 2021
92021
DPCIAN: A novel dual-channel pedestrian crossing intention anticipation network
B Yang, Z Wei, H Hu, R Wang, C Yang, R Ni
IEEE Transactions on Intelligent Transportation Systems, 2023
82023
Negative emotions sensitive humanoid robot with attention-enhanced facial expression recognition network
R Ni, X Liu, Y Chen, X Zhou, H Cai, LC Kiong
Intelligent Automation & Soft Computing 34 (1), 149-164, 2022
72022
Diverse local facial behaviors learning from enhanced expression flow for microexpression recognition
R Ni, B Yang, X Zhou, S Song, X Liu
Knowledge-Based Systems 275, 110729, 2023
62023
Vision-based recognition of pedestrian crossing intention in an urban environment
B Yang, R Ni
2019 IEEE 9th Annual International Conference on CYBER Technology in …, 2019
62019
A federated pedestrian trajectory prediction model with data privacy protection
R Ni, Y Lu, B Yang, C Yang, X Liu
Complex & Intelligent Systems 10 (2), 1787-1799, 2024
52024
Faster pedestrian crossing intention prediction based on efficient fusion of diverse intention influencing factors
B Yang, J Zhu, C Hu, Z Yu, H Hu, R Ni
IEEE Transactions on Transportation Electrification, 2024
42024
Efficient lower layers parameter decoupling personalized federated learning method of facial expression recognition for home care robots
X Zhou, J Li, G Lan, R Ni, A Cangelosi, J Wang, X Liu
Information Fusion 106, 102261, 2024
32024
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