3 citations · 3 across the 17 of their papers we have counts for
16 papers · 1 filter
Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection
Lihua Zhou, Mao Ye, Xiatian Zhu +7
Open-vocabulary object detection with vision-language models (VLMs) such as Grounding DINO suffers from performance degradation under test-time distribution shifts, primarily due t…
SurgMotion: A Video-Native Foundation Model for Universal Understanding of Surgical Videos
Jinlin Wu, Felix Holm, Chuxi Chen +17
While foundation models have advanced surgical video analysis, current approaches rely predominantly on pixel-level reconstruction objectives that waste model capacity on low-level…
NeuroABench: A Multimodal Evaluation Benchmark for Neurosurgical Anatomy Identification
Ziyang Song, Zelin Zang, Xiaofan Ye +7
Multimodal Large Language Models (MLLMs) have shown significant potential in surgical video understanding. With improved zero-shot performance and more effective human-machine inte…
Bayesian Test-time Adaptation for Object Recognition and Detection with Vision-language Models
Lihua Zhou, Mao Ye, Shuaifeng Li +7
Vision-language models (VLMs) such as CLIP and Grounding DINO have achieved remarkable success in object recognition and detection. However, their performance often degrades under…
A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications
Hongyuan Zhang, Yuheng Wu, Mingyang Zhao +22
Artificial intelligence (AI) that can effectively learn ultrasound representations by integrating multi-source data holds significant promise for advancing clinical care. However,…
SurgLLM: A Versatile Large Multimodal Model with Spatial Focus and Temporal Awareness for Surgical Video Understanding
Zhen Chen, Xingjian Luo, Kun Yuan +6
Surgical video understanding is crucial for facilitating Computer-Assisted Surgery (CAS) systems. Despite significant progress in existing studies, two major limitations persist, i…