: We are always looking for highly self-motivated students. Please contact me if you wish to consider:
with paid allowance & eligible to non-Singaporean undergrad and master students.
funded by CSC
scholarships are available.
with Singapore universities and A*STAR funded by SINGA
Find more details for eligible scholarships
12nd May 2023: OPENING (DDL: open until filled)
: One full-time research scientist
position on developing robust 3D deep learning algorithms is available with details
. Please contact me for informal inquiries.
- Jul 2023: Our work on On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
was accepted by ICCV 2023 as Oral presentation
. Congratulations to Yushu Li
- May 2023: A*STAR MTC Programmatic Fund "Towards Realistic Deep Learning for 3D Vision" (SGD$ 1.1M allocated) will kickstart in Aug 2023
- We shall develop 3D deep learning techniques robust to imperfect visibility, adversarial attacks and incremental data to enable deployment in real-world applications.
- Sep 2022: Our work on Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering
was accepted by NeurIPS 2022. Congratulations to Su Yongyi
- Jun 2022: Our work on Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding
was accepted by ICPR 2022 as Oral presentation.
- May 2022: Two works to appear in IEEE Transactions on Image Processing (TIP).
- Nov 2021: Our work on Automatic Data Augmentation for 3D Point Cloud
has appeared in BMVC 2021. Code is available here
- Jun 2021: The first 3D affordance prediction dataset 3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding
has appeared in CVPR 2021. Code is available here
- May 2021: A*STAR CDA Project "Exploiting Unlabeled Data, Cheaper Labels and Efficient Annotation for 3D
Point Cloud Deep Learning" (EUDEA) ($SGD 238k allocated)
- My project on exploring label-efficient learning on 3D point cloud data started from Apr. 2021.
- We will be looking into improving the efficiency of 3D point cloud learning from several perspectives.