Abstract Contrastive Language–Image Pre-training (CLIP) has shown remarkable success
in learning with cross-modal supervision from extensive amounts of image–text pairs …

Contrastive Language--Image Pre-training (CLIP) has shown remarkable success in
learning with cross-modal supervision from extensive amounts of image--text pairs collected …

Multimodal models are becoming increasingly effective, in part due to unified components,
such as the Transformer architecture. However, multimodal models still often consist of many …

The Contrastive Language-Image Pre-Training (CLIP) model, pretrained on large visual text
corpora, has demonstrated significant improvements in visual and linguistic tasks and has …

Medical image visual question answering (VQA) is a task to answer clinical questions, given
a radiographic image, which is a challenging problem that requires a model to integrate both …

Medical visual question answering (VQA) is a challenging task that requires answering
clinical questions of a given medical image, by taking consider of both visual and language …

In this paper, we propose an end-to-end Retrieval-Augmented Visual Language Model
(REVEAL) that learns to encode world knowledge into a large-scale memory, and to retrieve …

Due to the severe lack of labeled data, existing methods of medical visual question
answering usually rely on transfer learning to obtain effective image feature representation …

We present Answer-Me, a task-aware multi-task framework which unifies a variety of
question answering tasks, such as, visual question answering, visual entailment, visual …

Abstract Medical Visual Question Answering (VQA) is a multimodal task to answer clinical
questions about medical images. Existing methods have achieved good performance, but …