朴永日

个人信息Personal Information

副教授

博士生导师

硕士生导师

性别:男

毕业院校:釜庆国立大学

学位:博士

所在单位:信息与通信工程学院

学科:信号与信息处理. 计算机软件与理论. 计算机应用技术. 软件工程

办公地点:大连理工大学创新园大厦B座505室

联系方式:0411-84706002转2505

电子邮箱:yrpiao@dlut.edu.cn

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个人简介Personal Profile

朴永日,信息与通信工程学院,副教授,博士生导师。本科毕业于吉林大学,博士毕业于韩国釜庆国立大学,毕业后在韩国三维显示国家研究中心担任研究教授,2012年回国入职大连理工大学。主要研究方向为计算机视觉人工智能、图像处理,计算成像等领域。目前在相关领域共发表学术论文100余篇,包括计算机视觉顶会CVPR/ICCV/ECCV、人工智能顶会NeurIPS/AAAI/IJCAI、国际顶刊IEEE TPAMI/TIP/TC/TMM/TCSVT申请中国发明专利16项,已授权3项。主持国家自然科学基金重点项目/面上项目/青年项目辽宁省中央引导科技发展基金/自然科学基金面上项目大连市科技创新计划基金、企业委托(华为/美团/KETI/HOLOLAB)项目等。计算机视觉顶会ECCV2020杰出评审专家AAAI2022资深评审专家(SPC)辽宁省自然科学学术成果奖3项、全国移动终端设计创新大赛全国第一名担任IScIDE2017国际会议专题主席、VALSE2018执行主席等职务。目前为中国计算机学会(CCF)计算机视觉专委会和中国图象图形学学会(CSIG)机器视觉专委会委员;计算机视觉领域三大顶会(CVPR/ICCV/ECCV)、人工智能领域顶会(NIPS/AAAI/IJCAI)、国际顶刊IEEE TPAMI/TIP/TCYB/TMM的审稿人。

  • Google Scholar ID:https://scholar.google.no/citations?hl=en&pli=1&user=iQ1oyrgAAAAJ  

  • Semantic Scholar ID:https://www.semanticscholar.org/author/Yongri-Piao/3051892


科研项目

2022.01 - 2025.12, 面向水下复杂环境的光场显著目标检测研究, 国家自然科学基金面上项目 (进行)

2022.01 - 2022.12, 面向复杂场景的光场目标智能检测研究, 辽宁省中央引导地方科技发展基金 (进行)

2021.07 - 2023.06, 基于光谱偏振散射特性的水下多模智能成像研究,辽宁省自然科学基金面上项目 (进行)

2020.01 - 2023.12, 水下多模态光信息的融合成像方法研究, 国家自然科学基金面上项目 ()

2019.01 - 2021.12, 基于光场深度学习的显著性目标智能检测研究,大连市科技创新计划基金 (完成)

2018.01 - 2021.12, 仿人灵巧手的操作规划方法研究,国家自然科学基金重点项目 (完成)

2021.05 - 2021.07, 稠密多视角图像智能合成系统开发, 韩国电子技术研究院 (完成)

2020.08 - 2021.08, 基于安防场景下的复杂目标跟踪技术合作项目, 华为公司 (完成)

2019.11 - 2020.05, 面向于AR/VR的光场智能合成, 科技部重点研发项目 (完成)

2019.09 - 2019.12, 基于机器学习的车载应用开发, 韩国电子技术研究院 (完成)

2019.07 - 2019.12, 真实感头戴式显示系统设计分析,科技部重点研发项目 (完成)

2017.05 - 2019.04, 面向水下暗环境的光子计数集成成像技术研究, 辽宁省自然科学基金-面上项目 (完成)

2015.01 - 2017.12, 基于离轴分布感知结构的三维集成成像技术研究, 国家自然科学基金-青年项目 (完成)


代表性论文(近五年)

[29] Yongri Piao, W. Wei, M. Zhang, Y. Jiang, H. Lu, Noise-sensitive adversarial learning for weakly supervised salient object detection, IEEE Trans on Multimedia, Accept (20 Dec 2021), IF: 6.513.  (中科院一区

[28] J. Li, W. Ji, Q. Bi, C. Yan, M. Zhang, Yongri Piao, H. Lu, L. Cheng, Joint semantic mining for weakly supervised RGB-D salient object detection, NeurIPS 2021. (CCF-A)

[27] Yongri Piao, J. Wang, M. Zhang, H. Lu, MF-Net: Multi-filter directive network for weakly supervised salient object detection, ICCV 2021. (CCF-A)

[26] M. Zhang, J. Liu, Y. Wang, Yongri Piao, S. Yao, W. Ji, H. Lu, Z. Luo, Video salient object detection via dynamic context-sensitive filtering network, ICCV 2021(Oral). (CCF-A)

[25] M. Zhang, T. Liu, Yongri Piao, S. Yao, H. Lu, Auto-MSFNet: Search multi-scale fusion network for salient object detection, ACM MM 2021. (CCF-A)

[24] W. Ji, J. Li, S. Yu, M. Zhang, Yongri Piao, S. Yao, H. Lu, et. al., Calibrated RGB-D salient object detection, CVPR 2021. (CCF-A)

[23] Y. Zhang, Yongri Piao, X. Ji, M. Zhang, Dynamic fusion network for light field depth estimation, PRCV 2021.

[22] Yongri Piao, Y. Jiang, M. Zhang, J. Wang, and H. Lu, A patch-aware network for light field salient object detection, IEEE Trans on Cybernetics, Early Access (18 Aug 2021), IF: 11.448.  (中科院一区

[21] M. Zhang, Z. Hu, and Yongri Piao, Motion-blurring free 3D reconstruction via parallax information in synthetic aperture integral imaging, Optics and Lasers in Engineering, 142: 106608 (July 2021), IF: 4.836. (中科院二区)

[20] M. Zhang, Y. Zhang, Yongri Piao, B. Hu, and H. Lu, Feature reintegration over differential treatment: a top-down and adaptive fusion network for RGB-D SOD, ACM MM 2020. (CCF-A)

[19] M. Zhang, X. Sun, J. Liu, S. Xu, Yongri Piao, and H. Lu, Asymmetric two-stream architecture for accurate RGB-D saliency detection, ECCV 2020. (CCF-B)

[18] W. Ji, J. Li, M. Zhang, Yongri Piao, H. Lu, Accurate RGB-D salient object detection via collaborative learning, ECCV 2020. (CCF-B)

[17] C. Li, R. Cong, Yongri Piao, Q. Xu, and C. C. Loy, RGB-D salient object detection with cross-modality modulation and selection, ECCV 2020. (CCF-B)

[16] Yongri Piao, Z. Rong, M. Zhang, W. Ren, and H. Lu, A2dele: Adaptive and attentive depth distiller for efficient RGB-D salient object detection, CVPR 2020. (CCF-A)

[15] M. Zhang, W. Ren, Yongri Piao, Z. Rong, and H. Lu, Select, supplement and focus for RGB-D saliency detection, CVPR 2020. (CCF-A)

[14] Yongri Piao, Z. Rong, M. Zhang, and H. Lu, Exploit and replace: an asymmetrical two-stream architecture for versatile light field saliency detection, AAAI 2020. (CCF-A)

[13] M. Zhang, W. Ji, Yongri Piao, J. Li, and H. Lu, LFNet: Light-field fusion network for salient object detection, IEEE Trans on Image Processing, 29(1): 6276-6287 (2020), IF: 10.856. (CCF-A(中科院一区)

[12] Yongri Piao, X. Li, M. Zhang, J. Yu, and H. Lu, Saliency detection via depth-induced cellular automata on light field, IEEE Trans on Image Processing 29(1):1879-1889 (2020), IF: 10.856. (CCF-A(中科院一区)

[11] M. Zhang, Yongri Piao, and Z. Zhong, Three-dimensional integral imaging with circular non-uniform distribution, Optics and Lasers in Engineering 126: 105912 (2020), IF: 4.836. (中科院二区)

[10] M. Zhang, W. Ji, Yongri Piao, J. Li, and H. Lu, Memory-oriented decoder for light field salient object detection, NeurIPS 2019. (CCF-A

[09] Yongri Piao, W. Ji, J. Li, M. Zhang, and H. Lu, Depth-induced multi-scale recurrent attention network for saliency detection, ICCV 2019. (CCF-A)

[08] T. Wang, Yongri Piao, X. Li, L. Zhang, and H. Lu, Deep learning for light field saliency detection, ICCV 2019. (CCF-A)

[07] Yongri Piao, Z. Rong, M. Zhang, X. Li, and H. Lu, Deep Light-field-driven saliency detection from a single view, IJCAI  2019. (CCF-A)

[06] M. Zhang, Yongri Piao, C. Wei, and Z. Si, Occlusion removal based on epipolar plane images in integral imaging system, Optics and lasers Technology 120: 105680 (2019), IF: 3.867. (中科院二区)

[05] M. Zhang, D. Li, and Yongri Piao, Effective orthoscopic integral imaging reconstruction via adjustable depth position, Optik 178: 97-103 (2019). (中科院二区)

[04] Q. Wang, X. Lu, P. Li, Z. Gao, and Yongri Piao, An information geometry based distance between high-dimensional covariances for scalable classification, IEEE Transactions on Circuits and Systems for Video Technology 28(10): 2449-2459 (2018), IF: 4.685. (中科院一区)

[03] Yongri Piao, M. Zhang, X. Wang, P. Li, Extended depth of field integral imaging using multi-focus fusion, Optics Communications 411: 8-14 (2018). (中科院二区)

[02] M. Zhang, C. Wei, Yongri Piao, and J. Liu, Depth-of-field extension in integral imaging using multi-focus elemental images, Applied Optics 56: 6059-6064 (2017). (中科院二)

[01] Yongri Piao, L. Xing, M. Zhang, and B. -G. Lee, Three-dimensional reconstruction of far and big objects using synthetic aperture integral imaging, Optics and Laser in Engineering 88: 153-161 (2017), IF: 4.836. (中科院二区)



  • 教育经历Education Background
  • 工作经历Work Experience
  • 研究方向Research Focus
  • 社会兼职Social Affiliations
  • 计算机视觉

  • 模式识别

  • 计算成像

  • 数字图像处理