4 papers
Concept-wise Attention for Fine-grained Concept Bottleneck Models
Minghong Zhong, Guoshuai Zou, Kanghao Chen +2
Recently impressive performance has been achieved in Concept Bottleneck Models (CBM) by utilizing the image-text alignment learned by a large pre-trained vision-language model (i.e…
FA: Forced Prompt Learning of Vision-Language Models for Out-of-Distribution Detection
Xinhua Lu, Runhe Lai, Yanqi Wu +3
Pre-trained vision-language models (VLMs) have advanced out-of-distribution (OOD) detection recently. However, existing CLIP-based methods often focus on learning OOD-related knowl…
Hierarchical Vision-Language Learning for Medical Out-of-Distribution Detection
Runhe Lai, Xinhua Lu, Kanghao Chen +3
In trustworthy medical diagnosis systems, integrating out-of-distribution (OOD) detection aims to identify unknown diseases in samples, thereby mitigating the risk of misdiagnosis.…
Augmenting Continual Learning of Diseases with LLM-Generated Visual Concepts
Jiantao Tan, Peixian Ma, Kanghao Chen +2
Continual learning is essential for medical image classification systems to adapt to dynamically evolving clinical environments. The integration of multimodal information can signi…