Openings
TOP: We are always looking for highly self-motivated students. Please feel free to contact me if you wish to consider the following opportunities
-
Short-term internship with paid allowance & eligible to non-Singaporean undergrad and master students.
-
Visiting PhD/Master/Ungrad funded by
CSC,
ARAP and
SIPGA scholarships are available.
-
Pursuing PhD with Singapore universities and A*STAR funded by
SINGA or
ARAP scholarship.
- Find more details for eligible
scholarships
- Find details for the above scholarships in a single
PDF
Research
Projects
- 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.
- 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.
Publications
- Dec 2023: Our
work on
Towards Real-World Test-Time Adaptation: Tri-Net Self-Training with Balanced Normalization was accepted by
AAAI 2024. Congratulations to Yongyi Su!
- Dec 2023: We are happy to share our most recent work on
improving the generalization of Segment Anything model -
WeSAM
- Nov 2023: Our
work on
Transformation-Invariant Network for Few-Shot Object Detection in Remote Sensing Images was accepted by
IEEE Transactions on Geoscience and Remote Sensing. Congratulations to
Nanqing Liu!
- Oct 2023: Our
work on
Revisiting Pretraining for Semi-Supervised Learning in the Low-Label Regime was accepted by
Neurocomputing. Congratulations!
- 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 (1.8% acceptance rate). Congratulations to
Yushu Li!
- May 2023: Our
work on
Weakly Supervised 3D Point Cloud Segmentation via Multi-Prototype Learning was accepted by
IEEE Transactions on Circuits and System for Video Technology (TCSVT). Congratulations to
Yongyi Su!
- Jan 2023: Our
work on
Diverse and consistent multi-view networks for semi-supervised regression was accepted by
ECML PKDD Journal Track 2023.
- 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: Our
work on
SemiCurv: Semi-Supervised Curvilinear Structure Segmentation was accepted by
IEEE Transactions on Image Processing (TIP).
- May 2022: Our
work on
MA-GANet: A Multi-Attention Generative Adversarial Network for Defocus Blur Detection was accepted by
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.
- Mar 2021: Our
work on
Learning Clustering for Motion Segmentation has appeared in
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT).