Zobrazeno 1 - 10
of 130
pro vyhledávání: '"Zhuang, Bohan"'
Autor:
Dayarathna, Sanuwani, Islam, Kh Tohidul, Zhuang, Bohan, Yang, Guang, Cai, Jianfei, Law, Meng, Chen, Zhaolin
Magnetic Resonance Imaging (MRI) is instrumental in clinical diagnosis, offering diverse contrasts that provide comprehensive diagnostic information. However, acquiring multiple MRI contrasts is often constrained by high costs, long scanning duration
Externí odkaz:
http://arxiv.org/abs/2409.00585
Autor:
Chen, Pengcheng, Ye, Jin, Wang, Guoan, Li, Yanjun, Deng, Zhongying, Li, Wei, Li, Tianbin, Duan, Haodong, Huang, Ziyan, Su, Yanzhou, Wang, Benyou, Zhang, Shaoting, Fu, Bin, Cai, Jianfei, Zhuang, Bohan, Seibel, Eric J, He, Junjun, Qiao, Yu
Large Vision-Language Models (LVLMs) are capable of handling diverse data types such as imaging, text, and physiological signals, and can be applied in various fields. In the medical field, LVLMs have a high potential to offer substantial assistance
Externí odkaz:
http://arxiv.org/abs/2408.03361
Autor:
Zhang, Zeyu, Liu, Akide, Chen, Qi, Chen, Feng, Reid, Ian, Hartley, Richard, Zhuang, Bohan, Tang, Hao
Text-to-motion generation holds potential for film, gaming, and robotics, yet current methods often prioritize short motion generation, making it challenging to produce long motion sequences effectively: (1) Current methods struggle to handle long mo
Externí odkaz:
http://arxiv.org/abs/2407.10061
Autor:
Wang, Guoan, Ye, Jin, Cheng, Junlong, Li, Tianbin, Chen, Zhaolin, Cai, Jianfei, He, Junjun, Zhuang, Bohan
Publikováno v:
MICCAI 2024
Volumetric medical image segmentation is pivotal in enhancing disease diagnosis, treatment planning, and advancing medical research. While existing volumetric foundation models for medical image segmentation, such as SAM-Med3D and SegVol, have shown
Externí odkaz:
http://arxiv.org/abs/2407.04938
The typical process for developing LLMs involves pre-training a general foundation model on massive data, followed by fine-tuning on task-specific data to create specialized experts. Serving these experts poses challenges, as loading all experts onto
Externí odkaz:
http://arxiv.org/abs/2406.09041
We present a novel task called online video editing, which is designed to edit \textbf{streaming} frames while maintaining temporal consistency. Unlike existing offline video editing assuming all frames are pre-established and accessible, online vide
Externí odkaz:
http://arxiv.org/abs/2405.19726
KV cache stores key and value states from previous tokens to avoid re-computation, yet it demands substantial storage space, especially for long sequences. Adaptive KV cache compression seeks to discern the saliency of tokens, preserving vital inform
Externí odkaz:
http://arxiv.org/abs/2405.14256
A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-Value (KV) caching. The KV cache stores key-value states of previously generated tokens, significantly reducing the need for repetitive comput
Externí odkaz:
http://arxiv.org/abs/2405.14366
Empowered by Large Language Models (LLMs), recent advancements in Video-based LLMs (VideoLLMs) have driven progress in various video understanding tasks. These models encode video representations through pooling or query aggregation over a vast numbe
Externí odkaz:
http://arxiv.org/abs/2404.03384
Autor:
Chen, Yuedong, Xu, Haofei, Zheng, Chuanxia, Zhuang, Bohan, Pollefeys, Marc, Geiger, Andreas, Cham, Tat-Jen, Cai, Jianfei
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we build a cost volume representation via plane sweeping, where the cross-v
Externí odkaz:
http://arxiv.org/abs/2403.14627