Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Tong, Ruofeng"'
Autor:
Wang, Hongyi, Luo, Luyang, Wang, Fang, Tong, Ruofeng, Chen, Yen-Wei, Hu, Hongjie, Lin, Lanfen, Chen, Hao
Multiple Instance Learning (MIL) has demonstrated promise in Whole Slide Image (WSI) classification. However, a major challenge persists due to the high computational cost associated with processing these gigapixel images. Existing methods generally
Externí odkaz:
http://arxiv.org/abs/2312.01099
Autor:
Song, Dan, Zhang, Xuanpu, Zhou, Juan, Nie, Weizhi, Tong, Ruofeng, Kankanhalli, Mohan, Liu, An-An
Image-based virtual try-on aims to synthesize a naturally dressed person image with a clothing image, which revolutionizes online shopping and inspires related topics within image generation, showing both research significance and commercial potentia
Externí odkaz:
http://arxiv.org/abs/2311.04811
Autor:
Li, Yudi, Tang, Min, Yang, Yun, Tong, Ruofeng, Yang, Shuangcai, Li, Yao, An, Bailin, Kou, Qilong
We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-hu
Externí odkaz:
http://arxiv.org/abs/2305.18808
Autor:
Dong, Jiahua, Cheng, Guohua, Zhang, Yue, Peng, Chengtao, Song, Yu, Tong, Ruofeng, Lin, Lanfen, Chen, Yen-Wei
Multi-organ segmentation, which identifies and separates different organs in medical images, is a fundamental task in medical image analysis. Recently, the immense success of deep learning motivated its wide adoption in multi-organ segmentation tasks
Externí odkaz:
http://arxiv.org/abs/2304.07123
Autor:
Wang, Hongyi, Luo, Luyang, Wang, Fang, Tong, Ruofeng, Chen, Yen-Wei, Hu, Hongjie, Lin, Lanfen, Chen, Hao
Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSI classification is usually regarded as a Multiple Instance Learning (MIL) problem when only slide-
Externí odkaz:
http://arxiv.org/abs/2303.15749
Autor:
Wang, Hongyi, Lin, Lanfen, Hu, Hongjie, Chen, Qingqing, Li, Yinhao, Iwamoto, Yutaro, Han, Xian-Hua, Chen, Yen-Wei, Tong, Ruofeng
High resolution (HR) 3D images are widely used nowadays, such as medical images like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). However, segmentation of these 3D images remains a challenge due to their high spatial resolution and
Externí odkaz:
http://arxiv.org/abs/2210.14645
Autor:
Huang, Huimin, Xie1, Shiao, Lin, Lanfen, Iwamoto, Yutaro, Han, Xianhua, Chen, Yen-Wei, Tong, Ruofeng
Recently, a variety of vision transformers have been developed as their capability of modeling long-range dependency. In current transformer-based backbones for medical image segmentation, convolutional layers were replaced with pure transformers, or
Externí odkaz:
http://arxiv.org/abs/2207.14552
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability with fewer parameters and a shorter prediction time is a challenging and
Externí odkaz:
http://arxiv.org/abs/2203.03951
Unsupervised domain adaptation (UDA) aims to learn transferable knowledge from a labeled source domain and adapts a trained model to an unlabeled target domain. To bridge the gap between source and target domains, one prevailing strategy is to minimi
Externí odkaz:
http://arxiv.org/abs/2202.13310
Autor:
Li, Yudi, Tang, Min, Yang, Yun, Huang, Zi, Tong, Ruofeng, Yang, Shuangcai, Li, Yao, Manocha, Dinesh
We present a novel mesh-based learning approach (N-Cloth) for plausible 3D cloth deformation prediction. Our approach is general and can handle cloth or obstacles represented by triangle meshes with arbitrary topologies. We use graph convolution to t
Externí odkaz:
http://arxiv.org/abs/2112.06397