当前位置: 中文主页 > 研究方向

南智雄

Personal profile

个人简介

  南智雄,男,重庆大学计算机学院副教授。加州大学洛杉矶分校(UCLA)联合培养博士。主要研究方向是自动驾驶和人机交互,在T-IP, T-MM, T-CSVT, T-IV, T-NNLS, T-ITS, Pattern Recognition, Neural Network, RAL等期刊和AAAI, NIPS, ACM-MM, ICCV, IROS, IV, ITSC等会议上表论文30余篇。主持、参与多项国家重点研发计划...

more+

研究方向


    研究兴趣1:自动驾驶/智能驾驶/无人驾驶/自主智能/无人系统

   自动驾驶是一个典型的技术聚集体和应用辐射体。2013年以来一直从事自动驾驶相关研究,曾获全国冠军2项。

1-1.png

    主要研究问题包括:2D目标检测、2D目标分割、2D目标重要性预测;激光SLAM;车道线检测;3D目标检测、语义分割;3D目标轨迹预测;传统路径规划;智能体自主探索;多智能体路径规划

 DI MaskDINO.gifDriver attention prediction & on-road object importance estimation_v1.gif

轨迹预测.gif

Autonomous Exploration.gifslam.gif

path planning.gifLane Detection.gif


研究兴趣2:人机交互/具身智能

   人工智能发展的长远目标是使机器具备接近人类、甚至某些方面超越人类的智能,未来社会是人与机器高度融合、协作、共生的社会。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、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。围绕这一目标,我们在人物注意力预测、人物意图目标预测等方面展开了研究:

Attention and Intention Prediction.gif

     科研动态


  •     祝贺李**(研二)的论文被机器学习顶会 NIPS 2024接收!该论文提出联合目标检测与实例分割模型DI-MaskDINO,其性能超越 

        目标检测模型DINO、分割模型Mask2Former、联合检测与分割模型MaskDINO。

        GitHub链接:https://github.com/CQU-ADHRI-Lab/DI-MaskDINO;arXiv链接:https://arxiv.org/abs/2410.16707。

  •     祝贺陈**(研三)的论文被机器学习顶会 NIPS 2024接收!

  •     祝贺向**(研二)的论文被一区期刊 T-IV 2023接收!

  •     祝贺陈**(研一)的论文被计算机视觉顶会 ICCV 2023接收!

  •     祝贺向**(研一)的论文被机器人顶会IROS 2023接收!


一作/通讯代表性论文

  1. Zhixiong Nan, Leiyu Jia, and Bin Xiao. HEOI: Human Attention Prediction in Natural Daily Life with Fine-Grained Human-Environment-Object Interaction Model. IEEE Transactions on Image Processing(T-TIP), 2024. (CCF-A)

  2. Zhixiong Nan, Xianghong Li, Tao Xiang, and Jifeng Dai. DI-MaskDINO: A Joint Object Detection and Instance Segmentation Model. Neural Information Processing Systems(NeurIPS), 2024. (CCF-A)

  3. Zhixiong Nan, Yilong Chen, Tianfei Zhou, and Tao Xiang. On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance. Neural Information Processing Systems(NeurIPS), 2024. (CCF-A)

  4. Yilong Chen, Zhixiong Nan*, Tao Xiang. FBLNet: FeedBack Loop Networ for Driver Attention Prediction. IEEE/CVF International Conference on Computer Vision(ICCV), 2023. (CCF-A)

  5. 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. (JCR 1区,中科院1区,CCF A)

  6. 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)

  7. Zhixiong Nan, Tao Xiang. Third-Person View Attention Prediction in Natural Scenarios with Weak Information Dependency and Human-Scene Interaction Mechanism. IEEE Transactions on Circuits and Systems for Video Technology(T-CSVT), 2023. (JCR 1区,中科院1区,CCF B)

  8. Tao Xiang, Hongyan Pan, Zhixiong Nan*. Video Violence Rating: A Large-scale Public Database and A Multimodal Rating Model. IEEE Transactions on Multimedia (TMM), 2024. (JCR 1区,中科院1区,CCF B)

  9. Junhong Xiang, Zhixiong Nan†∗, Zhe Song, Jun Huang and Lingxi Li. Map-free Trajectory Prediction in Traffic with Multi-level Spatial-temporal Modeling. IEEE Transactions on Intelligent Vehicle(T-IV), 2023. (JCR 1区,中科院1)

  10. 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. (JCR 1区,中科院1区,CCF B)

  11. Junhong Xiang, Jingmin Zhang and Zhixiong Nan*. A Fast and Map-Free Model for Trajectory Prediction in Traffics. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.(CCF-C,机器人顶会)


部分合作论文

  1. Fulin Luo, Yi Liu, Xiuwen Gong, Zhixiong Nan, Tan Guo. EMVCC: Enhanced Multi-View Contrastive Clustering for Hyperspectral Images. ACM Mulitimedia(ACM-MM), 2024. (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. 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. (JCR 1区,中科院1区,CCF B)

  4. 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. (JCR 1区,中科院1区,CCF B)







访问量:     最后更新时间:--