7 papers
MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang +7
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual p…
Epistemic-aware Vision-Language Foundation Model for Fetal Ultrasound Interpretation
Xiao He, Huangxuan Zhao, Guojia Wan +8
Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are adapted to structured adult imaging and u…
EarthSynth: Generating Informative Earth Observation with Diffusion Models
Jiancheng Pan, Shiye Lei, Yuqian Fu +7
Remote sensing image (RSI) interpretation typically faces challenges due to the scarcity of labeled data, which limits the performance of RSI interpretation tasks. To tackle this c…
Control Copy-Paste: Controllable Diffusion-Based Augmentation Method for Remote Sensing Few-Shot Object Detection
Yanxing Liu, Jiancheng Pan, Bingchen Zhang
Few-shot object detection (FSOD) for optical remote sensing images aims to detect rare objects with only a few annotated bounding boxes. The limited training data makes it difficul…
NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results
Yuqian Fu, Xingyu Qiu, Bin Ren +59
Cross-Domain Few-Shot Object Detection (CD-FSOD) poses significant challenges to existing object detection and few-shot detection models when applied across domains. In conjunction…
Enhance Then Search: An Augmentation-Search Strategy with Foundation Models for Cross-Domain Few-Shot Object Detection
Jiancheng Pan, Yanxing Liu, Xiao He +4
Foundation models pretrained on extensive datasets, such as GroundingDINO and LAE-DINO, have performed remarkably in the cross-domain few-shot object detection (CD-FSOD) task. Thro…