The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Xiaohua Zhai sits on this spectrum.
This scientist: 64 publications — 1st percentile
1% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Xiaohua Zhai sits on this spectrum.
This scientist: 38 D-Index — 30th percentile
30% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Xiaohua Zhai is affiliated with Google in the United States and has contributed extensively to the field of computer science, with a strong focus on computer vision and artificial intelligence. Their research spans multiple subfields including computer vision and pattern recognition, artificial intelligence, radiology, nuclear medicine and imaging, materials chemistry, and electrical and electronic engineering.
Their work involves several main research topics such as domain adaptation and few-shot learning, multimodal machine learning applications, advanced neural network applications, advanced image and video retrieval techniques, natural language processing techniques, COVID-19 diagnosis using AI, and topic modeling.
Frequent coauthors collaborating with Xiaohua Zhai include Lucas Beyer, Neil Houlsby, А. И. Колесников, Andreas Steiner, and Ibrahim Alabdulmohsin, indicating a collaborative approach across diverse teams and research focuses.
Publications by Xiaohua Zhai have appeared primarily in the following venues:
Significant recent papers include:
The volume and scope of their publications demonstrate active engagement with ongoing advancements in vision transformer models and challenges in machine learning credibility. Xiaohua Zhai has contributed to foundational research shaping domain adaptation, multimodal learning, and neural network architectures.
Their academic footprint reflects a balance between theoretical frameworks and transformative applications across machine learning disciplines, focusing on both performance and transferability of models in computer vision and related fields.
Alexey Dosovitskiy;Lucas Beyer;Alexander Kolesnikov;Dirk Weissenborn
Ilya Tolstikhin;Neil Houlsby;Alexander Kolesnikov;Lucas Beyer
Alexander Kolesnikov;Lucas Beyer;Xiaohua Zhai;Joan Puigcerver
Alexander Kolesnikov;Xiaohua Zhai;Lucas Beyer
Xiaohua Zhai;Alexander Kolesnikov;Neil Houlsby;Lucas Beyer
Lucas Beyer;Xiaohua Zhai;Avital Oliver;Alexander Kolesnikov
Unknown
Alexander D'Amour;Katherine A. Heller;Dan Moldovan;Ben Adlam
Unknown
Ting Chen;Xiaohua Zhai;Marvin Ritter;Mario Lucic
Xiaohua Zhai;Xiao Wang;Basil Mustafa;Andreas Steiner
Unknown
Xiaohua Zhai;Avital Oliver;Alexander Kolesnikov;Lucas Beyer
Xiaohua Zhai;Yuxin Peng;Jianguo Xiao
Unknown
Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen
Lucas Beyer;Xiaohua Zhai;Amélie Royer;Larisa Markeeva
Yuxin Peng;Xiaohua Zhai;Yunzhen Zhao;Xin Huang
Karol Kurach;Mario Lucic;Xiaohua Zhai;Marcin Michalski
Xiaohua Zhai;Yuxin Peng;Jianguo Xiao
Mario Lucic;Michael Tschannen;Marvin Ritter;Xiaohua Zhai
Lucas Beyer;Olivier J. Hénaff;Alexander Kolesnikov;Xiaohua Zhai
Karol Kurach;Mario Lučić;Xiaohua Zhai;Marcin Michalski
Xiaohua Zhai;Joan Puigcerver;Alexander Kolesnikov;Pierre Ruyssen
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Institute for Computer Science, Artificial Intelligence and Technology (INSAIT)
Publications: 17
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