Yu Tian is a postdoctoral research fellow at Harvard University. He received his Ph.D. in computer science at the Australian Institute of Machine Learning (AIML), University of Adelaide. He was also affiliated with South Australian Health and Medical Research Institute (SAHMRI) during his Ph.D. Previously, Dr. Tian obtained his bachelor’s degree in computer science with First Class Honours at the University of Adelaide.
ResearchMy main research interests are in the fields of computer vision and medical image analysis, in particular abnormality and rarity learning tasks, such as image/video anomaly detection for surveillance and industrial applications or early detection of diseases.
|Invited to give a talk at USTC Suzhou Institute of Advanced Research about my recent works in anomaly/ood detection.|
|Invited to give a talk at GESA Research Workshop 2022 about Anomaly Detection in Medical Imaging.|
|I have finished my PhD and joined Harvard Ophthalmology AI Lab as a postdoctoral research fellow.|
|One paper on anomaly/ood segmentation for urban driving scenes accepted to ECCV 2022 - selected for oral presentation.|
|Invited to be the reviewer of IEEE Transactions on Image Processing.|
|Four papers are early accepted to MICCAI 2022.|
Selected Publications* denotes equal contribution; ^ denotes corresponding authorship.
Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving ScenesEuropean Conference on Computer Vision (
ECCV Oral) 2022
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionInternational Conference on Medical Image Computing and Computer-Assisted Intervention (
MICCAI Early Accept) 2022
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image ClassificationIEEE Conference on Computer Vision and Pattern Recognition (
Deep One-Class Classification via Interpolated Gaussian DescriptorIn Thirty-Sixth AAAI Conference on Artificial Intelligence (
AAAI Oral) 2022
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude LearningIn Proceedings of the IEEE/CVF international conference on computer vision (
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesIn International Conference on Medical Image Computing and Computer-Assisted Intervention (
Few-shot anomaly detection for polyp frames from colonoscopyIn International Conference on Medical Image Computing and Computer-Assisted Intervention (