5 citations · 10 across the 5 of their papers we have counts for
5 papers
Enhancing Community Vision Screening -- AI Driven Retinal Photography for Early Disease Detection and Patient Trust
Xiaofeng Lei, Yih-Chung Tham, Jocelyn Hui Lin Goh +7
Community vision screening plays a crucial role in identifying individuals with vision loss and preventing avoidable blindness, particularly in rural communities where access to ey…
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
Yang Bai, Yang Zhou, Jun Zhou +3
Large vision language models (VLMs) combine large language models with vision encoders, demonstrating promise across various tasks. However, they often underperform in task-specifi…
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Kai Yu, Yang Zhou, Yang Bai +5
Retinal foundation models aim to learn generalizable representations from diverse retinal images, facilitating label-efficient model adaptation across various ophthalmic tasks. Des…
VQA4CIR: Boosting Composed Image Retrieval with Visual Question Answering
Chun-Mei Feng, Yang Bai, Tao Luo +6
Albeit progress has been made in Composed Image Retrieval (CIR), we empirically find that a certain percentage of failure retrieval results are not consistent with their relative c…
Sentence-level Prompts Benefit Composed Image Retrieval
Yang Bai, Xinxing Xu, Yong Liu +5
Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adop…