61 citations · 102 across the 21 of their papers we have counts for
6 papers · 1 filter
Language as an Anchor: Preserving Relative Visual Geometry for Domain Incremental Learning
Shuyi Geng, Tao Zhou, Yi Zhou
A key challenge in Domain Incremental Learning (DIL) is to continually learn under shifting distributions while preserving knowledge from previous domains. Existing methods face a…
Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning
Ruiqi Wu, Yuang Yao, Tengfei Ma +11
Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities under reinforcement learning (RL) paradigm. However, most existing multimodal med…
Text-driven Multiplanar Visual Interaction for Semi-supervised Medical Image Segmentation
Kaiwen Huang, Yi Zhou, Huazhu Fu +3
Semi-supervised medical image segmentation is a crucial technique for alleviating the high cost of data annotation. When labeled data is limited, textual information can provide ad…
Continual Retinal Vision-Language Pre-training upon Incremental Imaging Modalities
Yuang Yao, Ruiqi Wu, Yi Zhou +1
Traditional fundus image analysis models focus on single-modal tasks, ignoring fundus modality complementarity, which limits their versatility. Recently, retinal foundation models…
Universal Incremental Learning: Mitigating Confusion from Inter- and Intra-task Distribution Randomness
Sheng Luo, Yi Zhou, Jinsong Shi +1
Existing incremental learning (IL) methods rely on strong assumptions: incoming tasks either introduce exclusively new classes or new domains (i.e., Class IL and Domain IL), or inc…
MM-Retinal V2: Transfer an Elite Knowledge Spark into Fundus Vision-Language Pretraining
Ruiqi Wu, Na Su, Chenran Zhang +10
Vision-language pretraining (VLP) has been investigated to generalize across diverse downstream tasks for fundus image analysis. Although recent methods showcase promising achievem…