Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
202117.6k citationsStephen Lin, Baining Guo et al.profile →
Swin Transformer V2: Scaling Up Capacity and Resolution
This map shows the geographic impact of Baining Guo's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Baining Guo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baining Guo more than expected).
This network shows the impact of papers produced by Baining Guo. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Baining Guo. The network helps show where Baining Guo may publish in the future.
Co-authorship network of co-authors of Baining Guo
This figure shows the co-authorship network connecting the top 25 collaborators of Baining Guo.
A scholar is included among the top collaborators of Baining Guo based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Baining Guo. Baining Guo is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Yang, Yuqi, Yuxiao Guo, Yang Liu, et al.. (2025). Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding. Computational Visual Media. 11(1). 83–101.19 indexed citations breakdown →
Dai, Bin, Chen Zhu, Baining Guo, & David Wipf. (2018). Compressing Neural Networks using the Variational Information Bottleneck.. International Conference on Machine Learning. 1135–1144.28 indexed citations
5.
Huang, Ying, Stephen Lin, Xiaoqing Ding, Baining Guo, & Harry Shum. (2017). Real-time Lip Synchronization Based on Hidden Markov Models.
Wang, Jinting, Xin Tong, Zhouchen Lin, Baining Guo, & Harry Shum. (2008). Modeling and Rendering Heterogeneous Translucent Materials using Diffusion Equation. ACM Transactions on Graphics. 27.28 indexed citations
11.
Zhou, Kun, Xin Huang, Weiwei Xu, Baining Guo, & Heung‐Yeung Shum. (2007). Direct manipulation of subdivision surfaces on GPUs. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 91–91.17 indexed citations
12.
Wang, Lifeng, Wenle Wang, Julie Dorsey, et al.. (2006). Real-time rendering of plant leaves. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5–5.7 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.