16 citations · 18 across the 5 of their papers we have counts for
6 papers
VPTracker: Global Vision-Language Tracking via Visual Prompt
Jingchao Wang, Kaiwen Zhou, Zhijian Wu +3
Vision-Language Tracking aims to continuously localize objects described by a visual template and a language description. Existing methods, however, are typically limited to local…
Medusa: Cross-Modal Transferable Adversarial Attacks on Multimodal Medical Retrieval-Augmented Generation
Yingjia Shang, Yi Liu, Huimin Wang +4
With the rapid advancement of retrieval-augmented vision-language models, multimodal medical retrieval-augmented generation (MMed-RAG) systems are increasingly adopted in clinical…
MedREK: Retrieval-Based Editing for Medical LLMs with Key-Aware Prompts
Shujun Xia, Haokun Lin, Yichen Wu +9
LLMs hold great promise for healthcare applications, but the rapid evolution of medical knowledge and errors in training data often cause them to generate outdated or inaccurate in…
The Curse of Depth in Large Language Models
Wenfang Sun, Xinyuan Song, Pengxiang Li +3
In this paper, we introduce the Curse of Depth, a concept that highlights, explains, and addresses the recent observation in modern Large Language Models (LLMs) where nearly half o…
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang, Chi Liu, Nuwa Xi +7
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prom…
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Hong-Yu Zhou, Avital Oliver, Jianxin Wu +1
Semi-Supervised Learning (SSL) has been proved to be an effective way to leverage both labeled and unlabeled data at the same time. Recent semi-supervised approaches focus on deep…