no code implementations • 1 Apr 2024 • Zihan Guan, Mengxuan Hu, Sheng Li, Anil Vullikanti
Diffusion Models are vulnerable to backdoor attacks, where malicious attackers inject backdoors by poisoning some parts of the training samples during the training stage.
no code implementations • 28 Mar 2024 • Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu, Ni Lao, Lan Mu, Sheng Li, Gengchen Mai
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs.
1 code implementation • 8 Aug 2023 • Zihan Guan, Mengnan Du, Ninghao Liu
An emerging detection strategy in the vision and NLP domains is based on an intriguing phenomenon: when training models on a mixture of backdoor and clean samples, the loss on backdoor samples drops significantly faster than on clean samples, allowing backdoor samples to be easily detected by selecting samples with the lowest loss values.
no code implementations • 21 Jul 2023 • Zihan Guan, Zihao Wu, Zhengliang Liu, Dufan Wu, Hui Ren, Quanzheng Li, Xiang Li, Ninghao Liu
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.
no code implementations • 5 May 2023 • Zihan Guan, Mengxuan Hu, Zhongliang Zhou, Jielu Zhang, Sheng Li, Ninghao Liu
Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks.