论文列表

a)排名及影响因子出处：科睿唯安(原汤森路透)Journal Citation Reports（JCR）“https://jcr.clarivate.com/JCRLandingPageAction.action“.

b)通讯作者标注*.

1.期刊论文

[1]Zhixiong Nan, Jingjing Jiang, Xiaofeng Gao, Sanping Zhou, Weiliang Zuo, Ping Wei, Nanning Zheng*.Predicting Task-driven Attention via Integrating Bottom-up Stimulus and Top-down Guidance.IEEE Transactions on Image Processing(T-TIP), 2021.(JCR1区，中科院1区，CCF A)

[2] Ben Yang, Xuetao Zhang, Feiping Nie, Badong Chen, Fei Wang,Zhixiong Nan, Nanning Zheng. ECCA: Efficient Correntropy-based Clustering Algorithm with Orthogonal Concept Factorization.IEEE Transactions on Neural Networks and Learning Systems(T-NNLS), 2022.(JCR1区，中科院1区，CCF B)

[3] Songyi Zhang, Zhiqiang Jian, Xiaodong Deng, Shitao Chen,Zhixiong Nan, Nanning Zheng. Hierarchical Motion Planning for Autonomous Driving in Large-Scale Complex Scenarios.IEEE Transactions on Intelligent Transportation Systems(T-ITS), 2021.(JCR1区，中科院1区，CCF B)

[4]Zhixiong Nan, Tianmin Shu, Ran Gong, Shu Wang, Ping Wei*, Song-Chun Zhu, Nanning Zheng. Learning to infer human attention in daily activities.Pattern Recognition(PR), 2020.(JCR1区，中科院1区，CCF B)

[5]Zhixiong Nan, Jizhi Peng, Jingjing Jiang, Hui Chen, Ben Yang, Jingmin Xin, Nanning Zheng*. A Joint Object Detection and Semantic Segmentation Model with Cross-Attention and Inner-Attention Mechanisms.Neurocomputing, 2021.(JCR1区，中科院2区，CCF C)

[6]Ben Yang, Xuetao Zhang∗, Badong Chen, Feiping Nie, Zhiping Lin,Zhixiong Nan.Efficient correntropy-based multi-view clustering with anchor graph embedding.Neural Networks,2021.(JCR1区，中科院2区，CCF B)

[7]Jingjing Jiang,Zhixiong Nan, Hui Chen, Shitao Chen, Nanning Zheng*. Predicting Short-Term Next-Active-Object Through Visual Attention and Hand Position.Neurocomputing, 2021.(JCR1区，中科院2区，CCF C)

[8]Zhiqiang Jian, Songyi Zhang, Shitao Chen*,Zhixiong Nan, Nanning Zheng. A Global-Local Coupling Two-Stage Path Planning Method for Mobile Robots.IEEE Robotics and Automation Letters(RAL), 2021.(JCR1区，中科院2区)

[9]Zhixiong Nan, Ping Wei, Linhai Xu, and Nanning Zheng. Efficient lane boundary detection with spatial temporal knowledge filtering.Sensors, 2016.(JCR 2区，中科院3区)

2.会议论文

[1]Zhixiong Nan#, Yang Liu#, Nanning Zheng, Song-Chun Zhu. Recognizing unseen attribute-object pair with generative model.AAAI Conference on Artificial Intelligence(AAAI), 2019. (#共同一作)(CCF-A)

[2]Jingjing Jiang, Ziyi Liu, Yifan Liu,Zhixiong Nan, Nanning Zheng*. X-GGM: Graph Generative Modeling for Out-of-Distribution Generalization in Visual Question Answering.ACM Mulitimedia(ACM-MM), 2021.(CCF-A)

[3]Jizhi Peng#,Zhixiong Nan#, Linhai Xu, Jingmin Xin, Nanning Zheng. A deep model for joint object detection and semantic segmentation in traffic scenes.International Joint Conference on Neural Networks(IJCNN), 2020. (#共同一作)(CCF-C)

[4]Xinrui Yan, Yuhao Huang, Shitao Chen,Zhixiong Nan, Jingmin Xin, Nanning Zheng*. DSP-Net: Dense-to-Sparse Proposal Generation Paradigm for 3D Object Detection on Point Cloud.International Joint Conference on Neural Networks(IJCNN), 2021.(CCF-C)

[5]Zhixiong Nan#, Yang Feng#, Jiawei He, Ping Wei, Linhai Xu, Hongbin Sun, and Nanning Zheng. Scene-guided region proposal re-ranking method for on-road vehicle candidate generation.IEEE Intelligent Vehicles Symposium(IV), 2019. (#共同一作)(智能车领域两个top会议之一)

[6]Zhixiong Nan, Menghan Pan, Xiao Wang, Ping Wei, Linhai Xu, Hongbing Sun, and Nanning Zheng. Exploring the potential of using semantic context and common sense in on-road vehicle detection.IEEE Intelligent Vehicles Symposium(IV), 2018.(智能车领域两个top会议之一)

[7]He Zhang,Zhixiong Nan*, Tao Yang, Yifan Liu, Nanning Zheng. A Driving Behavior Recognition Model with Bi-LSTM and Multi-Scale CNN.IEEE Intelligent Vehicles Symposium(IV), 2020.(智能车领域两个top会议之一)

[8]Tao Yang,Zhixiong Nan*, He Zhang, Shitao Chen, Nanning Zheng. Traffic Agent Trajectory Prediction Using Social Convolution and Attention Mechanism.IEEE Intelligent Vehicles Symposium(IV), 2020.(智能车领域两个top会议之一)

[9]Dantong Zhu, Yuhao Huang, Shengqi Wang, Shitao Chen,ZhixiongNan, Nanning Zheng. Multi-scale Key Points Regression for Lane Detection.IEEE Intelligent Vehicles Symposium(IV), 2021.(智能车领域两个top会议之一)

[10]Zhuo Yang, Yuhao Huang, Xinrui Yan, Shitao Chen*, Jun Sun, Jinpeng Dong, Ziyi Liu,Zhixiong Nan, Nanning Zheng. MuRF-Net: multi-receptive field pillars for 3D object detection from point cloud.IEEE Intelligent Vehicles Symposium(IV), 2020.(智能车领域两个top会议之一)

[11]Kongtao Zhu, Junxiang Zhan, Shitao Chen*,Zhixiong Nan, Nanning Zheng. ATV navigation in complex and unstructured environment containing stairs.IEEE Intelligent Vehicles Symposium(IV), 2020.(智能车领域两个top会议之一)

[12]Junxiang Zhan, Tangyike Zhang, Jiamin Shi, Xin Guan,Zhixiong Nan, Nanning Zheng∗. A Dual Closed-loop Longitudinal Speed Controller Using Smooth Feedforward and Fuzzy Logic for Autonomous Driving Vehicles.IEEE International Conference on Intelligent Transportation Systems(ITSC), 2021.(智能车领域两个top会议之一)

[13]Qian Hou, Songyi Zhang, Shitao Chen,Zhixiong Nan, Nanning Zheng*. Straight Skeleton Based Automatic Generation of Hierarchical Topological Map in Indoor Environment.IEEE International Conference on Intelligent Transportation Systems(ITSC), 2021.(智能车领域两个top会议之一)

[14]Zhiqiang Jian, Songyi Zhang, Shitao Chen, Tangyike Zhang,Zhixiong Nan, Nanning Zheng*.Obstacle-Centered Trajectory Planning for Autonomous Mobile Robot.IEEE International Conference on Intelligent Transportation Systems(ITSC), 2021.(智能车领域两个top会议之一)

[15]Xiao Wang, Jing Wen,Zhixiong Nan, Juwang Shi, Linhai Xu and Nanning Zheng. A general and practical path planning framework for autonomous vehicles.IEEE International Conference on Intelligent Transportation Systems(ITSC), 2016.

[16]Zhijing Xu, Yuhao Huang*, Shitao Chen,Zhixiong Nan, Nanning Zheng. Anchor-free pedestrian detection model with semantic context of traffic scenario.The China Automation congress(CAC),2020.

研究兴趣

• 目标检测与场景分割

目标检测和分割是自动驾驶汽车完成各种任务的感知基础，包括2D物体检测、3D物体检测、场景语义分割、场景实例分割。

• 目标轨迹、行为、意图预测

目标轨迹、行为、意图预测是实现更加安全可靠路径规划的基础。

• 智能调度与路径规划

对于单智能体，路径规划是运动的基础。对于多智能体，调度是运动的基础。

• 人机交互注意力、意图预测

如何使机器具备较高的智能水平，从而能自然和谐地与人进行交互、协助或者代替人类完成生产生活中各项任务是人类科技发展的长远目标。为了实现这一目标，首先需要研究如何使机器理解人类，具体理解人在看什么、干什么、想什么。注意力和意图预测是机器理解人的重要内容。