Research
My 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.
Selected Publications
* denotes equal contribution; ^ denotes corresponding authorship.
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FairSeg: A Large-scale Medical Image Segmentation Dataset for Fairness Learning with Fair Error-Bound Scaling
Yu Tian*, Yan Luo*, Min Shi*, Ava Kouhana, Tobias Elze, and Mengyu Wang
arXiv preprint , 2023
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Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling
Yan Luo*, Yu Tian*, Min Shi*, Tobias Elze, and Mengyu Wang
arXiv preprint , 2023
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Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization
Yan Luo*, Yu Tian*, Min Shi*, Tobias Elze, and Mengyu Wang
arXiv preprint , 2023
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AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, and Jiming Chen
arXiv preprint , 2023
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UnifiedVisionGPT: Streamlining Vision-Oriented AI through Generalized Multimodal Framework
Chris Kelly, Luhui Hu, Cindy Yang, Yu Tian, Deshun Yang, Bang Yang, Zaoshan Huang, Zihao Li, and Yuexian Zou
arXiv preprint arXiv:2310.12790 , 2023
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Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Jiawen Zhu, Choubo Ding, Yu Tian, and Guansong Pang
arXiv preprint arXiv:2310.12790 , 2023
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Harvard Glaucoma Detection and Progression: A Multimodal Multitask Dataset and Generalization-Reinforced Semi-Supervised Learning
Yan Luo*, Min Shi*, Yu Tian*, Tobias Elze, and Mengyu Wang
In Proceedings of the IEEE/CVF international conference on computer vision (ICCV), 2023
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Self-supervised Pseudo Multi-class Pre-training for Unsupervised Anomaly Detection and Segmentation in Medical Images
Yu Tian*, Fengbei Liu*, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W Verjans, Rajvinder Singh, and
Gustavo Carneiro
Medical Image Analysis (MedIA), 2023
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Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes
Yu Tian*, Yuyuan Liu*, Guansong Pang, Fengbei Liu, Yuanhong Chen, and
Gustavo Carneiro
European Conference on Computer Vision (ECCV Oral), 2022
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Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection
Yu Tian, Guansong Pang, Fengbei Liu, Yuyuan Liu, Chong Wang, Yuanhong Chen, Johan W Verjans, and
Gustavo Carneiro
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI Early Accept), 2022
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ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Fengbei Liu*,
Yu Tian*, Yuanhong Chen, Yuyuan Liu, Vasileios Belagiannis, and
Gustavo Carneiro
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022
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Deep One-Class Classification via Interpolated Gaussian Descriptor
In Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI Oral), 2022
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Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning
Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W Verjans, and
Gustavo Carneiro
In Proceedings of the IEEE/CVF international conference on computer vision (ICCV), 2021
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Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images
Yu Tian, Guansong Pang, Fengbei Liu, Seon Ho Shin, Johan W Verjans, Rajvinder Singh,
Gustavo Carneiro, and others
In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2021
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Few-shot anomaly detection for polyp frames from colonoscopy
Yu Tian, Gabriel Maicas, Leonardo Zorron Cheng Tao Pu, Rajvinder Singh, Johan W Verjans, and
Gustavo Carneiro
In International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2020