2 citations · 4 across the 5 of their papers we have counts for
5 papers
Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM
Jingxuan Kang, Ziqi Zhang, Shaoming Zheng +9
Segmentation is central to clinical diagnosis and monitoring, yet the reliability of modern foundation models in medical imaging still depends on the availability of precise prompt…
From Local Details to Global Context: Advancing Vision-Language Models with Attention-Based Selection
Lincan Cai, Jingxuan Kang, Shuang Li +4
Pretrained vision-language models (VLMs), e.g., CLIP, demonstrate impressive zero-shot capabilities on downstream tasks. Prior research highlights the crucial role of visual augmen…
Learning Modality Knowledge Alignment for Cross-Modality Transfer
Wenxuan Ma, Shuang Li, Lincan Cai +1
Cross-modality transfer aims to leverage large pretrained models to complete tasks that may not belong to the modality of pretraining data. Existing works achieve certain success i…
Enhancing Cross-Modal Fine-Tuning with Gradually Intermediate Modality Generation
Lincan Cai, Shuang Li, Wenxuan Ma +4
Large-scale pretrained models have proven immensely valuable in handling data-intensive modalities like text and image. However, fine-tuning these models for certain specialized mo…
Language Semantic Graph Guided Data-Efficient Learning
Wenxuan Ma, Shuang Li, Lincan Cai +1
Developing generalizable models that can effectively learn from limited data and with minimal reliance on human supervision is a significant objective within the machine learning c…