Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Luan, Tianyu"'
Autor:
Ding, Hao, Gao, Zhongpai, Planche, Benjamin, Luan, Tianyu, Sharma, Abhishek, Zheng, Meng, Lou, Ange, Chen, Terrence, Unberath, Mathias, Wu, Ziyan
Surgical phase recognition is essential for analyzing procedure-specific surgical videos. While recent transformer-based architectures have advanced sequence processing capabilities, they struggle with maintaining consistency across lengthy surgical
Externí odkaz:
http://arxiv.org/abs/2411.18018
Autor:
Lou, Ange, Planche, Benjamin, Gao, Zhongpai, Li, Yamin, Luan, Tianyu, Ding, Hao, Zheng, Meng, Chen, Terrence, Wu, Ziyan, Noble, Jack
Numerous recent approaches to modeling and re-rendering dynamic scenes leverage plane-based explicit representations, addressing slow training times associated with models like neural radiance fields (NeRF) and Gaussian splatting (GS). However, merel
Externí odkaz:
http://arxiv.org/abs/2410.14169
Autor:
Luan, Tianyu, Gao, Zhongpai, Xie, Luyuan, Sharma, Abhishek, Ding, Hao, Planche, Benjamin, Zheng, Meng, Lou, Ange, Chen, Terrence, Yuan, Junsong, Wu, Ziyan
We introduce a novel bottom-up approach for human body mesh reconstruction, specifically designed to address the challenges posed by partial visibility and occlusion in input images. Traditional top-down methods, relying on whole-body parametric mode
Externí odkaz:
http://arxiv.org/abs/2407.09694
Due to the scarcity of labeled data, self-supervised learning (SSL) has gained much attention in 3D medical image segmentation, by extracting semantic representations from unlabeled data. Among SSL strategies, Masked image modeling (MIM) has shown ef
Externí odkaz:
http://arxiv.org/abs/2407.06468
Autor:
Xie, Luyuan, Lin, Manqing, Xu, ChenMing, Luan, Tianyu, Zeng, Zhipeng, Qian, Wenjun, Li, Cong, Fang, Yuejian, Shen, Qingni, Wu, Zhonghai
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates collaborative model development without the need to share local data from h
Externí odkaz:
http://arxiv.org/abs/2407.00474
Autor:
Xie, Luyuan, Lin, Manqing, Liu, Siyuan, Xu, ChenMing, Luan, Tianyu, Li, Cong, Fang, Yuejian, Shen, Qingni, Wu, Zhonghai
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity and privacy concerns. However, existing methods often suffer from client
Externí odkaz:
http://arxiv.org/abs/2407.00462
Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network architectures (system heterogeneity), across clients pose significant chal
Externí odkaz:
http://arxiv.org/abs/2405.06822
While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e.g. tens of points, are observed. Surprisingly, via entropy analysis, we find that eve
Externí odkaz:
http://arxiv.org/abs/2403.07359
Autor:
Lou, Ange, Planche, Benjamin, Gao, Zhongpai, Li, Yamin, Luan, Tianyu, Ding, Hao, Chen, Terrence, Noble, Jack, Wu, Ziyan
Addressing the intricate challenge of modeling and re-rendering dynamic scenes, most recent approaches have sought to simplify these complexities using plane-based explicit representations, overcoming the slow training time issues associated with met
Externí odkaz:
http://arxiv.org/abs/2403.02265
Existing 3D mesh shape evaluation metrics mainly focus on the overall shape but are usually less sensitive to local details. This makes them inconsistent with human evaluation, as human perception cares about both overall and detailed shape. In this
Externí odkaz:
http://arxiv.org/abs/2403.01619