Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Xiang, Tiange"'
Most existing human rendering methods require every part of the human to be fully visible throughout the input video. However, this assumption does not hold in real-life settings where obstructions are common, resulting in only partial visibility of
Externí odkaz:
http://arxiv.org/abs/2407.00316
Autor:
Xiang, Tiange, Zhang, Yixiao, Lu, Yongyi, Yuille, Alan, Zhang, Chaoyi, Cai, Weidong, Zhou, Zongwei
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection o
Externí odkaz:
http://arxiv.org/abs/2403.08689
Rendering the visual appearance of moving humans from occluded monocular videos is a challenging task. Most existing research renders 3D humans under ideal conditions, requiring a clear and unobstructed scene. Those methods cannot be used to render h
Externí odkaz:
http://arxiv.org/abs/2401.00431
3D understanding and rendering of moving humans from monocular videos is a challenging task. Despite recent progress, the task remains difficult in real-world scenarios, where obstacles may block the camera view and cause partial occlusions in the ca
Externí odkaz:
http://arxiv.org/abs/2308.04622
Magnetic resonance imaging (MRI) is a common and life-saving medical imaging technique. However, acquiring high signal-to-noise ratio MRI scans requires long scan times, resulting in increased costs and patient discomfort, and decreased throughput. T
Externí odkaz:
http://arxiv.org/abs/2302.03018
Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding
Decoding visual stimuli from brain recordings aims to deepen our understanding of the human visual system and build a solid foundation for bridging human and computer vision through the Brain-Computer Interface. However, reconstructing high-quality i
Externí odkaz:
http://arxiv.org/abs/2211.06956
Autor:
Xiang, Tiange, Zhang, Chaoyi, Wang, Xinyi, Song, Yang, Liu, Dongnan, Huang, Heng, Cai, Weidong
U-Net, as an encoder-decoder architecture with forward skip connections, has achieved promising results in various medical image analysis tasks. Many recent approaches have also extended U-Net with more complex building blocks, which typically increa
Externí odkaz:
http://arxiv.org/abs/2203.05709
Autor:
Yu, Jianhui, Zhang, Chaoyi, Wang, Heng, Zhang, Dingxin, Song, Yang, Xiang, Tiange, Liu, Dongnan, Cai, Weidong
General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis. However, there are barely related works for medical point clouds, which are important for dis
Externí odkaz:
http://arxiv.org/abs/2112.04863
Autor:
Xiang, Tiange, Zhang, Yixiao, Lu, Yongyi, Yuille, Alan L., Zhang, Chaoyi, Cai, Weidong, Zhou, Zongwei
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. To exploit this structured information, we propose the use of Space-aware Mem
Externí odkaz:
http://arxiv.org/abs/2111.13495
Autor:
Xiang, Tiange, Song, Yang, Zhang, Chaoyi, Liu, Dongnan, Chen, Mei, Zhang, Fan, Huang, Heng, O'Donnell, Lauren, Cai, Weidong
We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classification with patch-level labels. However, patch-level labels require precis
Externí odkaz:
http://arxiv.org/abs/2109.05788