12 citations · 13 across the 6 of their papers we have counts for
6 papers
LLM-Bootstrapped Targeted Finding Guidance for Factual MLLM-based Medical Report Generation
Cunyuan Yang, Dejuan Song, Xiaotao Pang +7
The automatic generation of medical reports utilizing Multimodal Large Language Models (MLLMs) frequently encounters challenges related to factual instability, which may manifest a…
Solving Instance Detection from an Open-World Perspective
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to…
One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning
Chunpeng Zhou, Qianqian Shen, Zhi Yu +2
Recent advancements in fine-tuning Vision-Language Foundation Models (VLMs) have garnered significant attention for their effectiveness in downstream few-shot learning tasks.While…
SAM-SP: Self-Prompting Makes SAM Great Again
Chunpeng Zhou, Kangjie Ning, Qianqian Shen +3
The recently introduced Segment Anything Model (SAM), a Visual Foundation Model (VFM), has demonstrated impressive capabilities in zero-shot segmentation tasks across diverse natur…
A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture
Qianqian Shen, Yunhan Zhao, Nahyun Kwon +3
Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene.…
Q-Net: Query-Informed Few-Shot Medical Image Segmentation
Qianqian Shen, Yanan Li, Jiyong Jin +1
Deep learning has achieved tremendous success in computer vision, while medical image segmentation (MIS) remains a challenge, due to the scarcity of data annotations. Meta-learning…