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Found 3 papers, 1 papers with code
ConES: Concept Embedding Search for Parameter Efficient Tuning Large Vision Language Models
To achieve this, we propose a Concept Embedding Search (ConES) approach by optimizing prompt embeddings -- without the need of the text encoder -- to capture the 'concept' of the image modality through a variety of task objectives.
Towards General Purpose Medical AI: Continual Learning Medical Foundation Model
Therefore, in this work, we further explore the possibility of leveraging pre-trained VLMs as medical foundation models for building general-purpose medical AI, where we thoroughly investigate three machine-learning paradigms, i. e., domain/task-specialized learning, joint learning, and continual learning, for training the VLMs and evaluate their generalization performance on cross-domain and cross-task test sets.
Medical Image Understanding with Pretrained Vision Language Models: A Comprehensive Study
The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images.
