Zobrazeno 1 - 10
of 5 232
pro vyhledávání: '"He, Qian"'
Autor:
Kang, Guoxin, Gao, Wanling, Wang, Lei, Luo, Chunjie, Ye, Hainan, He, Qian, Dai, Shaopeng, Zhan, Jianfeng
By utilizing statistical methods to analyze bibliographic data, bibliometrics faces inherent limitations in identifying the most significant science and technology achievements and researchers. To overcome this challenge, we present an evaluatology-b
Externí odkaz:
http://arxiv.org/abs/2408.12158
Autor:
Mao, Zhendong, Huang, Mengqi, Ding, Fei, Liu, Mingcong, He, Qian, Chang, Xiaojun, Zhang, Yongdong
Text-to-image customization, which takes given texts and images depicting given subjects as inputs, aims to synthesize new images that align with both text semantics and subject appearance. This task provides precise control over details that text al
Externí odkaz:
http://arxiv.org/abs/2408.09744
The rapid advancement of deep learning has facilitated the automated processing of electron microscopy (EM) big data stacks. However, designing a framework that eliminates manual labeling and adapts to domain gaps remains challenging. Current researc
Externí odkaz:
http://arxiv.org/abs/2407.19544
Autor:
Yao, Bingqing, Xu, Chaokai, Tang, Yaxin, Du, Yankun, Tan, Shengdong, Dai, Sheng, Luo, Guangfu, He, Qian
Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demo
Externí odkaz:
http://arxiv.org/abs/2407.14181
Autor:
Ning, Shoucong, Xu, Wenhui, Sheng, Pengju, Loh, Leyi, Pennycook, Stephen, Zhang, Fucai, Bosman, Michel, He, Qian
As a burgeoning technique, out-of-focus electron ptychography offers the potential for rapidly imaging atomic-scale large fields of view (FoV) using a single diffraction dataset. However, achieving robust out-of-focus ptychographic reconstruction pos
Externí odkaz:
http://arxiv.org/abs/2406.15879
Autor:
Merrill, Mike A., Paruchuri, Akshay, Rezaei, Naghmeh, Kovacs, Geza, Perez, Javier, Liu, Yun, Schenck, Erik, Hammerquist, Nova, Sunshine, Jake, Tailor, Shyam, Ayush, Kumar, Su, Hao-Wei, He, Qian, McLean, Cory Y., Malhotra, Mark, Patel, Shwetak, Zhan, Jiening, Althoff, Tim, McDuff, Daniel, Liu, Xin
Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of th
Externí odkaz:
http://arxiv.org/abs/2406.06464
Existing solutions to image editing tasks suffer from several issues. Though achieving remarkably satisfying generated results, some supervised methods require huge amounts of paired training data, which greatly limits their usages. The other unsuper
Externí odkaz:
http://arxiv.org/abs/2405.12490
Publikováno v:
JMIR Medical Informatics, Vol 9, Iss 5, p e29794 (2021)
Externí odkaz:
https://doaj.org/article/b865974b909042248a313670ac73b29b
We propose Pure and Lightning ID customization (PuLID), a novel tuning-free ID customization method for text-to-image generation. By incorporating a Lightning T2I branch with a standard diffusion one, PuLID introduces both contrastive alignment loss
Externí odkaz:
http://arxiv.org/abs/2404.16022
Autor:
Qi, Tianhao, Fang, Shancheng, Wu, Yanze, Xie, Hongtao, Liu, Jiawei, Chen, Lang, He, Qian, Zhang, Yongdong
The diffusion-based text-to-image model harbors immense potential in transferring reference style. However, current encoder-based approaches significantly impair the text controllability of text-to-image models while transferring styles. In this pape
Externí odkaz:
http://arxiv.org/abs/2403.06951