Zobrazeno 1 - 10
of 5 394
pro vyhledávání: '"ZHANG, Yuxin"'
Autor:
Zhang, Yuxin
The invention of Li-ion batteries (LIBs) marks a new era of energy storage and allows for the large-scale industrialization of electric vehicles. However, the flammable organic electrolyte in LIBs raises significant safety concerns and has resulted i
Externí odkaz:
https://hdl.handle.net/10919/116738
Clustered federated learning (CFL) addresses the performance challenges posed by data heterogeneity in federated learning (FL) by organizing edge devices with similar data distributions into clusters, enabling collaborative model training tailored to
Externí odkaz:
http://arxiv.org/abs/2501.01850
Autor:
Lin, Zheng, Zhang, Yuxin, Chen, Zhe, Fang, Zihan, Wu, Cong, Chen, Xianhao, Gao, Yue, Luo, Jun
Recently, the increasing deployment of LEO satellite systems has enabled various space analytics (e.g., crop and climate monitoring), which heavily relies on the advancements in deep learning (DL). However, the intermittent connectivity between LEO s
Externí odkaz:
http://arxiv.org/abs/2501.01293
Autor:
Zhong, Yunshan, Zhou, Yuyao, Zhang, Yuxin, Li, Shen, Li, Yong, Chao, Fei, Zeng, Zhanpeng, Ji, Rongrong
Data-free quantization (DFQ), which facilitates model quantization without real data to address increasing concerns about data security, has garnered significant attention within the model compression community. Recently, the unique architecture of v
Externí odkaz:
http://arxiv.org/abs/2412.16553
Despite the efficiency of prompt learning in transferring vision-language models (VLMs) to downstream tasks, existing methods mainly learn the prompts in a coarse-grained manner where the learned prompt vectors are shared across all categories. Conse
Externí odkaz:
http://arxiv.org/abs/2412.08176
Video-to-music generation presents significant potential in video production, requiring the generated music to be both semantically and rhythmically aligned with the video. Achieving this alignment demands advanced music generation capabilities, soph
Externí odkaz:
http://arxiv.org/abs/2412.06296
Autor:
Xu, Yu, Tang, Fan, Cao, Juan, Zhang, Yuxin, Kong, Xiaoyu, Li, Jintao, Deussen, Oliver, Lee, Tong-Yee
Diffusion Transformers (DiTs) have exhibited robust capabilities in image generation tasks. However, accurate text-guided image editing for multimodal DiTs (MM-DiTs) still poses a significant challenge. Unlike UNet-based structures that could utilize
Externí odkaz:
http://arxiv.org/abs/2411.15034
Autor:
Zhang, Yuxin, Zheng, Dandan, Gong, Biao, Chen, Jingdong, Yang, Ming, Dong, Weiming, Xu, Changsheng
Lighting plays a pivotal role in ensuring the naturalness of video generation, significantly influencing the aesthetic quality of the generated content. However, due to the deep coupling between lighting and the temporal features of videos, it remain
Externí odkaz:
http://arxiv.org/abs/2410.22979
Object detection algorithms are pivotal components of unmanned aerial vehicle (UAV) imaging systems, extensively employed in complex fields. However, images captured by high-mobility UAVs often suffer from motion blur cases, which significantly imped
Externí odkaz:
http://arxiv.org/abs/2410.17822
Autor:
Zhang, Yuxin, Lin, Zheng, Chen, Zhe, Fang, Zihan, Zhu, Wenjun, Chen, Xianhao, Zhao, Jin, Gao, Yue
Traditional federated learning (FL) frameworks rely heavily on terrestrial networks, where coverage limitations and increasing bandwidth congestion significantly hinder model convergence. Fortunately, the advancement of low-Earth orbit (LEO) satellit
Externí odkaz:
http://arxiv.org/abs/2409.13503