Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Ding, Xinpeng"'
General networks for 3D medical image segmentation have recently undergone extensive exploration. Behind the exceptional performance of these networks lies a significant demand for a large volume of pixel-level annotated data, which is time-consuming
Externí odkaz:
http://arxiv.org/abs/2409.08492
Autor:
Huang, Runhui, Ding, Xinpeng, Wang, Chunwei, Han, Jianhua, Liu, Yulong, Zhao, Hengshuang, Xu, Hang, Hou, Lu, Zhang, Wei, Liang, Xiaodan
High-resolution inputs enable Large Vision-Language Models (LVLMs) to discern finer visual details, enhancing their comprehension capabilities. To reduce the training and computation costs caused by high-resolution input, one promising direction is t
Externí odkaz:
http://arxiv.org/abs/2407.08706
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 to reconstruct CT, also known as sparse-view reconstruction, can reduc
Externí odkaz:
http://arxiv.org/abs/2406.03902
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 omits key multi-view and temporal information which is crucial for robu
Externí odkaz:
http://arxiv.org/abs/2401.00988
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 between video-level and boundary-level annotation, explicit-supervision me
Externí odkaz:
http://arxiv.org/abs/2312.02483
Echocardiogram video segmentation plays an important role in cardiac disease diagnosis. This paper studies the unsupervised domain adaption (UDA) for echocardiogram video segmentation, where the goal is to generalize the model trained on the source d
Externí odkaz:
http://arxiv.org/abs/2309.11145
Cardiac structure segmentation from echocardiogram videos plays a crucial role in diagnosing heart disease. The combination of multi-view echocardiogram data is essential to enhance the accuracy and robustness of automated methods. However, due to th
Externí odkaz:
http://arxiv.org/abs/2309.11144
Autonomous driving systems generally employ separate models for different tasks resulting in intricate designs. For the first time, we leverage singular multimodal large language models (MLLMs) to consolidate multiple autonomous driving tasks from vi
Externí odkaz:
http://arxiv.org/abs/2309.05186
Publikováno v:
ACM MM2023
Recently, large-scale pre-trained vision-language models have presented benefits for alleviating class imbalance in long-tailed recognition. However, the long-tailed data distribution can corrupt the representation space, where the distance between h
Externí odkaz:
http://arxiv.org/abs/2308.12522
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 adaptation (SF-UDA) aims at adapting a model trained on the source side to alig
Externí odkaz:
http://arxiv.org/abs/2308.07731