22 citations · 46 across the 18 of their papers we have counts for
19 papers
Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaboration
Honglong Yang, Shanshan Song, Yi Qin +6
Generalist Medical AI (GMAI) systems have demonstrated expert-level performance in biomedical perception tasks, yet their clinical utility remains limited by inadequate multi-modal…
Tri-Plane Mamba: Efficiently Adapting Segment Anything Model for 3D Medical Images
Hualiang Wang, Yiqun Lin, Xinpeng Ding +1
General networks for 3D medical image segmentation have recently undergone extensive exploration. Behind the exceptional performance of these networks lies a significant demand for…
C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction
Yiqun Lin, Jiewen Yang, Hualiang Wang +3
Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views…
Holistic Autonomous Driving Understanding by Bird's-Eye-View Injected Multi-Modal Large Models
Xinpeng Ding, Jinahua Han, Hang Xu +3
The rise of multimodal large language models (MLLMs) has spurred interest in language-based driving tasks. However, existing research typically focuses on limited tasks and often o…
EtC: Temporal Boundary Expand then Clarify for Weakly Supervised Video Grounding with Multimodal Large Language Model
Guozhang Li, Xinpeng Ding, De Cheng +3
Early weakly supervised video grounding (WSVG) methods often struggle with incomplete boundary detection due to the absence of temporal boundary annotations. To bridge the gap betw…
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
Jiewen Yang, Xinpeng Ding, Ziyang Zheng +2
Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentat…
GL-Fusion: Global-Local Fusion Network for Multi-view Echocardiogram Video Segmentation
Ziyang Zheng, Jiewen Yang, Xinpeng Ding +2
Cardiac structure segmentation from echocardiogram videos plays a crucial role in diagnosing heart disease. The combination of multi-view echocardiogram data is essential to enhanc…
HiLM-D: Enhancing MLLMs with Multi-Scale High-Resolution Details for Autonomous Driving
Xinpeng Ding, Jianhua Han, Hang Xu +2
Recent efforts to use natural language for interpretable driving focus mainly on planning, neglecting perception tasks. In this paper, we address this gap by introducing ROLISP (Ri…
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
Zheang Huai, Xinpeng Ding, Yi Li +1
In the domain adaptation problem, source data may be unavailable to the target client side due to privacy or intellectual property issues. Source-free unsupervised domain adaptatio…
Uniformly Distributed Category Prototype-Guided Vision-Language Framework for Long-Tail Recognition
Siming Fu, Xiaoxuan He, Xinpeng Ding +2
Recently, large-scale pre-trained vision-language models have presented benefits for alleviating class imbalance in long-tailed recognition. However, the long-tailed data distribut…
Boosting Weakly-Supervised Temporal Action Localization with Text Information
Guozhang Li, De Cheng, Xinpeng Ding +3
Due to the lack of temporal annotation, current Weakly-supervised Temporal Action Localization (WTAL) methods are generally stuck into over-complete or incomplete localization. In…
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint
Guozhang Li, De Cheng, Xinpeng Ding +3
Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the trai…
Cyclical Self-Supervision for Semi-Supervised Ejection Fraction Prediction from Echocardiogram Videos
Weihang Dai, Xiaomeng Li, Xinpeng Ding +1
Left-ventricular ejection fraction (LVEF) is an important indicator of heart failure. Existing methods for LVEF estimation from video require large amounts of annotated data to ach…
Learning Shadow Correspondence for Video Shadow Detection
Xinpeng Ding, Jingweng Yang, Xiaowei Hu +1
Video shadow detection aims to generate consistent shadow predictions among video frames. However, the current approaches suffer from inconsistent shadow predictions across frames,…
Free Lunch for Surgical Video Understanding by Distilling Self-Supervisions
Xinpeng Ding, Ziwei Liu, Xiaomeng Li
Self-supervised learning has witnessed great progress in vision and NLP; recently, it also attracted much attention to various medical imaging modalities such as X-ray, CT, and MRI…
Less is More: Surgical Phase Recognition from Timestamp Supervision
Xinpeng Ding, Xinjian Yan, Zixun Wang +4
Surgical phase recognition is a fundamental task in computer-assisted surgery systems. Most existing works are under the supervision of expensive and time-consuming full annotation…
Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos
Xinpeng Ding, Xiaomeng Li
Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learnin…
Support-Set Based Cross-Supervision for Video Grounding
Xinpeng Ding, Nannan Wang, Shiwei Zhang +5
Current approaches for video grounding propose kinds of complex architectures to capture the video-text relations, and have achieved impressive improvements. However, it is hard to…
Weakly Supervised Temporal Action Localization with Segment-Level Labels
Xinpeng Ding, Nannan Wang, Xinbo Gao +3
Temporal action localization presents a trade-off between test performance and annotation-time cost. Fully supervised methods achieve good performance with time-consuming boundary…