Zobrazeno 1 - 10
of 584
pro vyhledávání: '"Huang, Yawen"'
Autor:
Yi, Jingjun, Bi, Qi, Zheng, Hao, Zhan, Haolan, Ji, Wei, Huang, Yawen, Li, Yuexiang, Zheng, Yefeng
The rapid development of Vision Foundation Model (VFM) brings inherent out-domain generalization for a variety of down-stream tasks. Among them, domain generalized semantic segmentation (DGSS) holds unique challenges as the cross-domain images share
Externí odkaz:
http://arxiv.org/abs/2407.18568
Few-shot medical image segmentation has achieved great progress in improving accuracy and efficiency of medical analysis in the biomedical imaging field. However, most existing methods cannot explore inter-class relations among base and novel medical
Externí odkaz:
http://arxiv.org/abs/2406.05054
Autor:
Duan, Haoran, Wang, Shidong, Ojha, Varun, Wang, Shizheng, Huang, Yawen, Long, Yang, Ranjan, Rajiv, Zheng, Yefeng
While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning
Externí odkaz:
http://arxiv.org/abs/2405.15962
Anomaly synthesis is one of the effective methods to augment abnormal samples for training. However, current anomaly synthesis methods predominantly rely on texture information as input, which limits the fidelity of synthesized abnormal samples. Beca
Externí odkaz:
http://arxiv.org/abs/2404.19444
Autor:
Xie, Jinheng, Feng, Jiajun, Tian, Zhaoxu, Lin, Kevin Qinghong, Huang, Yawen, Xia, Xi, Gong, Nanxu, Zuo, Xu, Yang, Jiaqi, Zheng, Yefeng, Shou, Mike Zheng
Concepts involved in long-form videos such as people, objects, and their interactions, can be viewed as following an implicit prior. They are notably complex and continue to pose challenges to be comprehensively learned. In recent years, generative p
Externí odkaz:
http://arxiv.org/abs/2404.15909
Autor:
Tu, Peng, Zhou, Xun, Wang, Mingming, Yang, Xiaojun, Peng, Bo, Chen, Ping, Su, Xiu, Huang, Yawen, Zheng, Yefeng, Xu, Chang
Neural Radiance Fields (NeRF) have emerged as a paradigm-shifting methodology for the photorealistic rendering of objects and environments, enabling the synthesis of novel viewpoints with remarkable fidelity. This is accomplished through the strategi
Externí odkaz:
http://arxiv.org/abs/2404.04875
Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition. This work aims to achieve zero-shot controlled stylization in 3D scenes utilizing text or visual input as conditioning factors. We intr
Externí odkaz:
http://arxiv.org/abs/2402.01950
The goal of our work is to generate high-quality novel views from monocular videos of complex and dynamic scenes. Prior methods, such as DynamicNeRF, have shown impressive performance by leveraging time-varying dynamic radiation fields. However, thes
Externí odkaz:
http://arxiv.org/abs/2401.04861
Autor:
Gong, Xuan, Li, Shanglin, Bao, Yuxiang, Yao, Barry, Huang, Yawen, Wu, Ziyan, Zhang, Baochang, Zheng, Yefeng, Doermann, David
Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data. Existing FL methods either iteratively share local model parameters or deplo
Externí odkaz:
http://arxiv.org/abs/2312.14478
ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction
Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, s
Externí odkaz:
http://arxiv.org/abs/2312.06164