Research:
I'm generally interested in understanding how visual perception emerges.
In particular, on topics:
learning visual representations from self-supervised training.
multi-modal co-training with visual apperance, motion, audio, textual description, etc.
open-world, object-centric representation learning.
learning visual representation for embodied agents.
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To Prospective Student:
If you are enthusiastic to work with me on the above topics, please drop me an email with a CV and Research Proposal.
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News:
June 2022, Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images. To appear at MICCAI2022
May 2022, Transforming the Interactive Segmentation for Medical Imaging. To appear at MICCAI2022 (Early Accept)
Apr 2022, Quantum Self-supervised Learning. Accepted by Quantum Science and Technology
Mar 2022, Unsupervised Salient Object Detection with Spectral Cluster Voting. CVPR2022 Workshop
Dec 2021, Temporal Alignment Networks for Long-term Video. CVPR2022.
Dec 2021, Label, Verify, Correct: A Simple Few Shot Object Detection Method. CVPR2022.
Dec 2021, It's About Time: Analog Clock Reading in the Wild. CVPR2022.
Dec 2021, Prompting Visual-Language Models for Efficient Video Understanding. Preprint.
Oct 2021, Segmenting Invisible Moving Objects. BMVC2021.
Oct 2021, Audio-Visual Synchronisation In The Wild. BMVC2021.
Oct 2021, All You Need Are a Few Pixels: Semantic Segmentation with PixelPick.
ICCV2021, ILDAV Workshop, (Best Paper Award)
Sep 2021, ImplicitVol: Sensorless 3D Ultrasound Reconstruction with Deep Implicit Representation. Preprint
Sep 2021, Self-supervised Tumor Segmentation through Layer Decomposition. Preprint
July 2021, Self-supervised Video Object Segmentation by Motion Grouping.
ICCV2021. (Best Paper Award at CVPR Workshop)
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