← Search

Zhengfeng Lai*

2 accepted papers

2024

Bridging the Pathology Domain Gap: Efficiently Adapting CLIP for Pathology Image Analysis with Limited Labeled Data

ECCV 2024poster

"Contrastive Language-Image Pre-training (CLIP) has shown its proficiency in acquiring distinctive visual representations and exhibiting strong generalization across diverse vision tasks. However, its effectiveness in pathology image analysis, particularly with limited labeled data, remains an ongoi…

Cited by 0SourcePDFScholar
2024

VeCLIP: Improving CLIP Training via Visual-enriched Captions

ECCV 2024poster

"Large-scale web-crawled datasets are fundamental for the success of pre-training vision-language models, such as CLIP. However, the inherent noise and potential irrelevance of web-crawled AltTexts pose challenges in achieving precise image-text alignment. Existing methods utilizing large language m…