Yongjian Deng 邓勇舰

Position: Lecturer
Affiliation: College of Computer Science, Beijing University of Technology
Email:       yjdeng@bjut.edu.cn
Address:   Room 233, Information Building, #100 Pingleyuan, Chaoyang District, Beijing, China, 100124

Dr. Deng holds a lecturer position in computer science at the Beijing University of Technology. He received B.Eng. degree, M.Sc. degree from the China University of Petroleum (Beijing), CN, and the University of Florida, USA, in 2016 and 2018, respectively. He obtained the Ph.D. degree from the City University of Hong Kong in 2021, under the supervision of Prof. LI, You-Fu.

Research Interests

His research interests include object recognition, action recognition, optical flow estimation, and deep learning with event-based cameras. He also works on multi-modal fusion, salient object detection, 3D point cloud learning, and semantic segmentation.

主攻的研究方向包含基于事件相机的物体识别、动作识别、光流估计及与计算机视觉相关的深度学习技术。同时，在多模态学习、显著性物体检测、三维点云学习及语义分割领域也有颇深的研究基础。

Openings 2025！

拟接收辅导博士研究生1名、硕士研究生2名，研究方向集中在：仿生视觉、3D视觉、多模态视觉特征融合及感知增强。

Collabration

I am looking for self-motivated and hardworking students to work with me on exciting topics in Computer Vision and Robot Vision. If you are interested in these areas, please send an email to me！

非常欢迎对计算机视觉、机器人视觉有兴趣的同学加入到我们的实验室共同在这些领域进行研究，如果对上述领域有兴趣，请通过邮件和我联系！

Publications （#co-first author, *corresponding author, ^supervised student）

  • H. Chen, F. Shen, D. Ding, Yongjian Deng and C. Li, “Disentangled Cross-Modal Transformer for RGB-D Salient Object Detection and Beyond,” in IEEE Transactions on Image Processing (TIP), vol. 33, pp. 1699-1709, 2024. SCI-Q1 Top CCF-A
  • B. Xie, Yongjian Deng, Zhanpeng Shao, Youfu Li “EISNet: A Multi-Modal Fusion Network for Semantic Segmentation with Events and Images,” in IEEE Transactions on Multimedia (IEEE TMM), 2024. SCI-Q1 Top CCF-B
  • B. Yao^, Yongjian Deng*, Y. Liu, H. Chen, and Z. Yang “SAM-Event-Adapter: Adapting Segment Anything Model for Event-RGB Semantic Segmentation,” in IEEE International Conference on Robotics and Automation (ICRA), 2024. CCF-B
  • Y. Liu^, Yongjian Deng*, H. Chen, and Z. Yang, “Video Frame Interpolation via Direct Synthesis with the Event-based Reference,” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Accept, 2024. CCF-A
  • Yongjian Deng, H. Chen, and Y. Li, “A Dynamic Graph CNN with Cross-Representation Distillation for Event-Based Recognition,”in The AAAI Conference on Artificial Intelligence (AAAI), Accept, 2024. CCF-A
  • C. Zhang, H. Liu, Yongjian Deng, et al., “TokenHPE: Learning Orientation Tokens for Efficient Head Pose Estimation via Transformers,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023. CCF-A
  • C. Zhang, H. Liu, Yongjian Deng, et al., “TransIFC: Invariant Cues-aware Feature Concentration Learning for Efficient Fine-grained Bird Image Classification,” in IEEE Transactions on Multimedia (IEEE TMM), 2022. SCI-Q1 Top CCF-B
  • Yongjian Deng, H. Chen*, H. Liu, and Y. Li*, A Voxel Graph CNN for Object Classification With Event Cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1172-1181, 2022. CCF-A
  • B. Xie#, Yongjian Deng#, Z. Shao, H. Liu, Y. Li* and H. Chen, “VMV-GCN: Volumetric Multi-View Based Graph CNN for Event Stream Classification,” in IEEE Robotics and Automation Letters (IEEE RA-L, with oral presentation in ICRA), December 2021. SCI-Q2 Top
  • Yongjian Deng, H. Chen and Y. Li*, “MVF-Net: A Multi-view Fusion Network for Event-based Object Classification,” in IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT), 2021, doi: 10.1109/TCSVT.2021.3073673. SCI-Q1 Top CCF-B
  • Yongjian Deng, H. Chen, H. Chen and Y. Li*, “Learning From Images: A Distillation Learning Framework for Event Cameras,” in IEEE Transactions on Image Processing (IEEE TIP), vol. 30, pp. 4919-4931, 2021, doi: 10.1109/TIP.2021.3077136. SCI-Q1 Top CCF-A
  • H. Chen, Y. Li,*, Yongjian Deng, et al. CNN-Based RGB-D Salient Object Detection: Learn, Select, and Fuse. International Journal of Computer Vision (IJCV), 129, 2076–2096 (2021). https://doi.org/10.1007/s11263-021-01452-0. SCI-Q1 Top CCF-A
  • Yongjian Deng, Y. Li* and H. Chen, “AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification,” in IEEE Robotics and Automation Letters (IEEE RA-L, with oral presentation in IROS), vol. 5, no. 3, pp. 4596-4603, July 2020, doi: 10.1109/LRA.2020.3002480. SCI-Q2 Top
  • H. Chen, Yongjian Deng, Y. Li*, T. -Y. Hung and G. Lin, “RGBD Salient Object Detection via Disentangled Cross-Modal Fusion,” in IEEE Transactions on Image Processing (IEEE TIP), vol. 29, pp. 8407-8416, 2020, doi: 10.1109/TIP.2020.3014734. SCI-Q1 Top CCF-A
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