3 citations · 5 across the 5 of their papers we have counts for
5 papers
KU-DMIS-MSRA at RadSum23: Pre-trained Vision-Language Model for Radiology Report Summarization
Gangwoo Kim, Hajung Kim, Lei Ji +7
In this paper, we introduce CheXOFA, a new pre-trained vision-language model (VLM) for the chest X-ray domain. Our model is initially pre-trained on various multimodal datasets wit…
ELVIS: Empowering Locality of Vision Language Pre-training with Intra-modal Similarity
Sumin Seo, JaeWoong Shin, Jaewoo Kang +2
Deep learning has shown great potential in assisting radiologists in reading chest X-ray (CXR) images, but its need for expensive annotations for improving performance prevents wid…
LIQUID: A Framework for List Question Answering Dataset Generation
Seongyun Lee, Hyunjae Kim, Jaewoo Kang
Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotatio…
Empowering Sentence Encoders with Prompting and Label Retrieval for Zero-shot Text Classification
Jimin Hong, Jungsoo Park, Daeyoung Kim +3
With contrastive pre-training, sentence encoders are generally optimized to locate semantically similar samples closer to each other in their embedding spaces. In this work, we foc…
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble
Hyunsoo Cho, Choonghyun Park, Jaewook Kang +3
Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. Most…