Dr Hang Dai

  • Lecturer in Machine Learning (School of Computing Science)

Biography

News

  • 29/01/2024 One paper is accepted in ICRA 2024, "Improving Radial Imbalances with Hybrid Voxelization and RadialMix for LiDAR 3D Semantic Segmentation".
  • 09/12/2023 One paper is accepted in IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), "CTVSR: Collaborative Spatial-Temporal Transformer for Video Super-Resolution".
  • 13/04/2023 One paper is accepted in IEEE Transactions on Medical Imaging (T-MI).
  • 27/02/2023 Two papers accepted in CVPR 2023. (1) Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers. Zhou Huang et al. (2) MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving. Jiale Li et al. Congrats to former Interns.
  • 17/01/2023 One paper "CEAFFOD: Cross-Ensemble Attention-based Feature Fusion Architecture Towards a Robust and Real-time UAV-based Object Detection in Complex Scenarios" is accepted in ICRA 2023. Congrats to Master student Ahmed.
  • 10/01/2023 One paper is accepted in AAAI 2023 in the main track. Congrats to former Intern Xiaobin Hu.
  • 08/07/2022 Three papers are accepted in ECCV 2022. Congrats to former Intern Jiale Li, Yu Hong, and Postdoc Dr. Xuebin Qin. Papers: CMKD (Oral presentation, acceptance rate: 2.7%), Self-Distillation,  DIS.

Bio

‪Hang Dai‬ - ‪Google Scholar‬

Dr. Hang Dai is a Lecturer in Machine Learning at the School of Computing Science (IDA Section), University of Glasgow, Scotland, UK. He holds a Ph.D. in Computer Science from the University of York, where he built a 3D morphable model of human heads (LYHM) from the Headspace dataset. He was awarded a three-year full Oversea Research Scholarship from the University of York during his Ph.D. study.

He is a regular publisher in top AI/CV conferences like CVPR, ECCV, ICCV, ACM MM and etc. He also wins competitive top conference challenge awards, such as CVPR 2022 Waymo Autonomous Driving Challenge and ECCV 2020 Command for Autonomous Vehicles Challenge. He leads his research group to formulate the problem of highly accurate dichotomous image segmentation towards entering an era of demanding highly accurate outputs from AI algorithms to support delicate human-machine interaction and immersed virtual life and deliver the DIS benchmark to the community.

                           Highly Accurate Dichotomous Image Segmentation

Research interests

His research interests lie in the intersection of computer vision, pattern recognition, and artificial intelligence.

Research Interests.

3D Computer Vision: 3D shape analysis, 3D from X, face manipulation from 3D.

Autonomous Driving: 3D semantic segmentation, 3D object detection, Occupancy network, Interactive Planning, Lanes Network, Autolabeling, Simulation.

Low-level Vision Task: Object referral, Highly accurate image segmentation in Natural images, and Bio-medical images.

Ph.D. Vacancies.

I am looking for highly motivated and competitive Ph.D. candidates. Publications in top conferences like CVPR, ECCV, and ICCV are a PLUS. For Ph.D. topics, please refer to the section of Supervision. If you are interested, drop me an email with your CV. I will reply when the Ph.D. applicant meets the requirements. 

[Hot!] One full Ph.D. studentship at Home Rate is available for the coming academic year (23-24). Qualified international students need to pay for the fee differences. This position is very competitive. Application Deadline: January 2023.

[CSC Ph.D. Studentship] Deadline: January of the academic year.

• What is covered: if accepted by the CSC, the CSC covers the stipend. The college’s graduate school endeavors to match this with a tuition waiver.
• Eligibility of Student: student has to be Chinese and has to agree to the CSC’s terms which include the requirement to return to China after completing their Ph.D.

Students who are not already in Glasgow, and who are affiliated with partner institutions are encouraged to apply for CSC Ph.D. studentship.

[Other scholarships] Deadline is usually January of the academic year. CDT studentships’ deadline is decided by the CDT team. More scholarship opportunities can be found here: University of Glasgow - Schools - School of Computing Science - Postgraduate research - Prospective students - PhD projects and funding opportunities.

[Ph.D. application process] Please refer to University of Glasgow - Schools - School of Computing Science - Postgraduate research - Prospective students. For international students, please make sure that you have the documentation to meet the language requirements for the College of Science & Engineering before you start the application process.

 

Publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020
Number of items: 21.

2024

Tragakis, A., Liu, Q., Kaul, C., Kumar Roy, S., Dai, H., Deligianni, F. , Murray-Smith, R. and Faccio, D. (2024) GLFNet: Global-Local (Frequency) Filter Networks for efficient Medical Image Segmentation. In: 21st IEEE International Symposium on Biomedical Imaging, Athens, Greece, 27-30 May 2024, (Accepted for Publication)

2023

Huang, Z., Dai, H., Xiang, T.-Z., Wang, S., Chen, H.-X., Qin, J. and Xiong, H. (2023) Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers. In: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023), Vancouver, Canada, 18-22 June 2023, pp. 5557-5566. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.00538)

Li, J., Dai, H., Han, H. and Ding, Y. (2023) MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving. In: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023), Vancouver, Canada, 18-22 June 2023, pp. 21694-21704. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.02078)

Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y. and Shao, L. (2023) High-resolution Iterative Feedback Network for Camouflaged Object Detection. In: 37th AAAI Conference on Artificial Intelligence (AAAI-23), Washington, DC, USA, 7-14 Feb 2023, pp. 881-889. ISBN 9781577358800 (doi: 10.1609/aaai.v37i1.25167)

Chen, G., Qin, J., Amor, B. B., Zhou, W., Dai, H., Zhou, T., Huang, H. and Shao, L. (2023) Automatic detection of tooth-gingiva trim lines on dental surfaces. IEEE Transactions on Medical Imaging, (doi: 10.1109/tmi.2023.3263161) (PMID:37015112) (Early Online Publication)

2022

Hong, Y., Dai, H. and Ding, Y. (2022) Cross-Modality Knowledge Distillation network for monocular 3D object detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13670. Springer, pp. 87-104. ISBN 9783031200809 (doi: 10.1007/978-3-031-20080-9_6)

Qin, X., Dai, H., Hu, X., Fan, D., Shao, L. and Gool, L. V. (2022) Highly accurate dichotomous image segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13678. Springer, pp. 38-56. ISBN 9783031197970 (doi: 10.1007/978-3-031-19797-0_3)

Li, J., Dai, H. and Ding, Y. (2022) Self-distillation for robust LiDAR semantic segmentation in autonomous driving. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13688. Springer, pp. 659-676. ISBN 9783031198151 (doi: 10.1007/978-3-031-19815-1_38)

Chen, Y.-N., Dai, H. and Ding, Y. (2022) Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18-24 Jun 2022, pp. 877-887. ISBN 9781665469463 (doi: 10.1109/CVPR52688.2022.00096)

Huang, Z., Xiang, T.-Z., Chen, H.-X. and Dai, H. (2022) Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 191, pp. 290-301. (doi: 10.1016/j.isprsjprs.2022.07.014)

Chen, G., Dai, H., Zhou, T., Shen, J. and Shao, L. (2022) Automatic Schelling point detection from meshes. IEEE Transactions on Visualization and Computer Graphics, 29(6), pp. 2926-2939. (doi: 10.1109/TVCG.2022.3144143) (PMID:35044917)

Duncan, C., Pears, N. E., Dai, H., Smith, W. A. P. and O′Higgins, P. (2022) Applications of 3D photography in craniofacial surgery. Journal of Pediatric Neurosciences, 17(5), pp. 21-28. (doi: 10.4103/jpn.JPN_48_22) (PMID:36388007) (PMCID:PMC9648652)

2021

Luo, S., Dai, H., Shao, L. and Ding, Y. (2021) M3DSSD: Monocular 3D Single Stage Object Detector. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20-25 Jun 2021, pp. 6141-6150. ISBN 9781665445092 (doi: 10.1109/CVPR46437.2021.00608)

Khan, S. A. and Dai, H. (2021) Video Transformer for Deepfake Detection with Incremental Learning. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 1821-1828. ISBN 9781450386517 (doi: 10.1145/3474085.3475332)

Li, J., Dai, H., Shao, L. and Ding, Y. (2021) Anchor-Free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 553-562. ISBN 9781450386517 (doi: 10.1145/3474085.3475208)

Li, J., Dai, H., Shao, L. and Ding, Y. (2021) From Voxel to Point: IoU-Guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 4622-4631. ISBN 9781450386517 (doi: 10.1145/3474085.3475314)

Kaul, C., Pears, N., Dai, H., Murray-Smith, R. and Manandhar, S. (2021) Focusnet++: attentive aggregated transformations for efficient and accurate medical image segmentation. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 13-16 April 2021, pp. 1042-1046. ISBN 9781665412469 (doi: 10.1109/ISBI48211.2021.9433918)

Dai, H., Luo, S., Ding, Y. and Shao, L. (2021) Commands for autonomous vehicles by progressively stacking visual-linguistic representations. In: Bartoli, A. and Fusiello, A. (eds.) Computer Vision – ECCV 2020 Workshops. Series: Lecture notes in computer science, 12536. Springer, pp. 27-32. ISBN 9783030660963 (doi: 10.1007/978-3-030-66096-3_2)

Luo, S., Dai, H., Shao, L. and Ding, Y. (2021) C4AV: learning cross-modal representations from transformers. In: Bartoli, A. and Fusiello, A. (eds.) Computer Vision – ECCV 2020 Workshops. Series: Lecture notes in computer science, 12536. Springer, pp. 33-38. ISBN 9783030660963 (doi: 10.1007/978-3-030-66096-3_3)

2020

Dai, H., Pears, N., Huber, P. and Smith, W. A. P. (2020) 3D morphable models: the face, ear and head. In: Liu, Y., Pears, N., Rosin, P. L. and Huber, P. (eds.) 3D Imaging, Analysis and Applications. Springer, pp. 463-512. ISBN 9783030440701 (doi: 10.1007/978-3-030-44070-1_10)

Dai, H., Pears, N., Smith, W. and Duncan, C. (2020) Statistical modeling of craniofacial shape and texture. International Journal of Computer Vision, 128(2), pp. 547-571. (doi: 10.1007/s11263-019-01260-7)

This list was generated on Sat Apr 20 00:57:34 2024 BST.
Jump to: Articles | Book Sections | Conference Proceedings
Number of items: 21.

Articles

Chen, G., Qin, J., Amor, B. B., Zhou, W., Dai, H., Zhou, T., Huang, H. and Shao, L. (2023) Automatic detection of tooth-gingiva trim lines on dental surfaces. IEEE Transactions on Medical Imaging, (doi: 10.1109/tmi.2023.3263161) (PMID:37015112) (Early Online Publication)

Huang, Z., Xiang, T.-Z., Chen, H.-X. and Dai, H. (2022) Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 191, pp. 290-301. (doi: 10.1016/j.isprsjprs.2022.07.014)

Chen, G., Dai, H., Zhou, T., Shen, J. and Shao, L. (2022) Automatic Schelling point detection from meshes. IEEE Transactions on Visualization and Computer Graphics, 29(6), pp. 2926-2939. (doi: 10.1109/TVCG.2022.3144143) (PMID:35044917)

Duncan, C., Pears, N. E., Dai, H., Smith, W. A. P. and O′Higgins, P. (2022) Applications of 3D photography in craniofacial surgery. Journal of Pediatric Neurosciences, 17(5), pp. 21-28. (doi: 10.4103/jpn.JPN_48_22) (PMID:36388007) (PMCID:PMC9648652)

Dai, H., Pears, N., Smith, W. and Duncan, C. (2020) Statistical modeling of craniofacial shape and texture. International Journal of Computer Vision, 128(2), pp. 547-571. (doi: 10.1007/s11263-019-01260-7)

Book Sections

Hong, Y., Dai, H. and Ding, Y. (2022) Cross-Modality Knowledge Distillation network for monocular 3D object detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13670. Springer, pp. 87-104. ISBN 9783031200809 (doi: 10.1007/978-3-031-20080-9_6)

Qin, X., Dai, H., Hu, X., Fan, D., Shao, L. and Gool, L. V. (2022) Highly accurate dichotomous image segmentation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13678. Springer, pp. 38-56. ISBN 9783031197970 (doi: 10.1007/978-3-031-19797-0_3)

Li, J., Dai, H. and Ding, Y. (2022) Self-distillation for robust LiDAR semantic segmentation in autonomous driving. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G. M. and Hassner, T. (eds.) Computer Vision – ECCV 2022. Series: Lecture notes in computer science, 13688. Springer, pp. 659-676. ISBN 9783031198151 (doi: 10.1007/978-3-031-19815-1_38)

Dai, H., Luo, S., Ding, Y. and Shao, L. (2021) Commands for autonomous vehicles by progressively stacking visual-linguistic representations. In: Bartoli, A. and Fusiello, A. (eds.) Computer Vision – ECCV 2020 Workshops. Series: Lecture notes in computer science, 12536. Springer, pp. 27-32. ISBN 9783030660963 (doi: 10.1007/978-3-030-66096-3_2)

Luo, S., Dai, H., Shao, L. and Ding, Y. (2021) C4AV: learning cross-modal representations from transformers. In: Bartoli, A. and Fusiello, A. (eds.) Computer Vision – ECCV 2020 Workshops. Series: Lecture notes in computer science, 12536. Springer, pp. 33-38. ISBN 9783030660963 (doi: 10.1007/978-3-030-66096-3_3)

Dai, H., Pears, N., Huber, P. and Smith, W. A. P. (2020) 3D morphable models: the face, ear and head. In: Liu, Y., Pears, N., Rosin, P. L. and Huber, P. (eds.) 3D Imaging, Analysis and Applications. Springer, pp. 463-512. ISBN 9783030440701 (doi: 10.1007/978-3-030-44070-1_10)

Conference Proceedings

Tragakis, A., Liu, Q., Kaul, C., Kumar Roy, S., Dai, H., Deligianni, F. , Murray-Smith, R. and Faccio, D. (2024) GLFNet: Global-Local (Frequency) Filter Networks for efficient Medical Image Segmentation. In: 21st IEEE International Symposium on Biomedical Imaging, Athens, Greece, 27-30 May 2024, (Accepted for Publication)

Huang, Z., Dai, H., Xiang, T.-Z., Wang, S., Chen, H.-X., Qin, J. and Xiong, H. (2023) Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers. In: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023), Vancouver, Canada, 18-22 June 2023, pp. 5557-5566. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.00538)

Li, J., Dai, H., Han, H. and Ding, Y. (2023) MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Driving. In: IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2023), Vancouver, Canada, 18-22 June 2023, pp. 21694-21704. ISBN 9798350301298 (doi: 10.1109/CVPR52729.2023.02078)

Hu, X., Wang, S., Qin, X., Dai, H., Ren, W., Luo, D., Tai, Y. and Shao, L. (2023) High-resolution Iterative Feedback Network for Camouflaged Object Detection. In: 37th AAAI Conference on Artificial Intelligence (AAAI-23), Washington, DC, USA, 7-14 Feb 2023, pp. 881-889. ISBN 9781577358800 (doi: 10.1609/aaai.v37i1.25167)

Chen, Y.-N., Dai, H. and Ding, Y. (2022) Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18-24 Jun 2022, pp. 877-887. ISBN 9781665469463 (doi: 10.1109/CVPR52688.2022.00096)

Luo, S., Dai, H., Shao, L. and Ding, Y. (2021) M3DSSD: Monocular 3D Single Stage Object Detector. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20-25 Jun 2021, pp. 6141-6150. ISBN 9781665445092 (doi: 10.1109/CVPR46437.2021.00608)

Khan, S. A. and Dai, H. (2021) Video Transformer for Deepfake Detection with Incremental Learning. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 1821-1828. ISBN 9781450386517 (doi: 10.1145/3474085.3475332)

Li, J., Dai, H., Shao, L. and Ding, Y. (2021) Anchor-Free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 553-562. ISBN 9781450386517 (doi: 10.1145/3474085.3475208)

Li, J., Dai, H., Shao, L. and Ding, Y. (2021) From Voxel to Point: IoU-Guided 3D Object Detection for Point Cloud with Voxel-to-Point Decoder. In: MM '21: Proceedings of the 29th ACM International Conference on Multimedia, Online, 20-24 Oct 2021, pp. 4622-4631. ISBN 9781450386517 (doi: 10.1145/3474085.3475314)

Kaul, C., Pears, N., Dai, H., Murray-Smith, R. and Manandhar, S. (2021) Focusnet++: attentive aggregated transformations for efficient and accurate medical image segmentation. In: IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France, 13-16 April 2021, pp. 1042-1046. ISBN 9781665412469 (doi: 10.1109/ISBI48211.2021.9433918)

This list was generated on Sat Apr 20 00:57:34 2024 BST.

Supervision

Postgraduate Students:

Qianying Liu, PhD student (Co-supervised with Fani Deligianni)

Zeyu Dong, PhD student

 

Ph.D. projects are not limited to the following aspects:

3D Computer Vision:

  • Semantic 3D face reconstructions from a single image
  • 3D hand reconstruction/tracking from RGB image or combined sensor data
  • 3D face recognition from combined sensor data
  • Deepfake generation or detection using 3D face reconstruction

 

                                    3D face reconstructions from a single image         

Autonomous Driving:

  • 3D semantic segmentation from point cloud (Self-Distillation) or sensor fusion (LiDAR+RGB).
  • 3D object detection from monocular image (M3dssd, Pseudo-Stereo, CMKD), stereo image (DSGN), point cloud (voxel-to-point, Anchor-free 3DSSD), or sensor fusion (LiDAR+RGB).
  • Occupancy network (MonoScene)
  • Interactive Planning: Optimization-based Trajectory Planner with constraints generated incrementally over search steps.
  • Lanes Network (HDMapGen, VectorMapNet)
  • Auto-labeling the captured sensor data using semi-supervised learning or weakly-supervised learning.  
  • Simulation: Sim2Real for autonomous driving.

                                        Monocular 3D Object Detection

Low-level vision task:

  • Object referral for Salient Object Detection, Commands for Autonomous Vehicles (1st, 2nd place).
  • Highly accurate image segmentation in natural  (including semantic segmentation, instance segmentation, salient object detection, and camouflaged object detection), and bio-medical images (for example, retina vessel segmentation).

 

  • Dong, Zeyu
    Vision-based 3D Flow and Occupancy Prediction
  • Liu, Qianying
    Deep Learning for Health Informatics
  • Xie, Yuxuan
    Pedestrian Interaction Modelling via Ego-centric View for Autonomous Driving

Teaching

COMPSCI5012 Internet Technology M - 2022-23