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.
Deep Learning Face Attributes in the Wild
20153.9k citationsPing Luo, Xiaogang Wang et al.profile →
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
This map shows the geographic impact of Ping Luo'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 Ping Luo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ping Luo more than expected).
This network shows the impact of papers produced by Ping Luo. 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 Ping Luo. The network helps show where Ping Luo may publish in the future.
Co-authorship network of co-authors of Ping Luo
This figure shows the co-authorship network connecting the top 25 collaborators of Ping Luo.
A scholar is included among the top collaborators of Ping Luo 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 Ping Luo. Ping Luo 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.
Tang, Yunlong, Jie An, Feng Zheng, et al.. (2025). Video Understanding With Large Language Models: A Survey. IEEE Transactions on Circuits and Systems for Video Technology. 36(2). 1355–1376.16 indexed citations breakdown →
Tong, Wenwen, Chonghao Sima, Tai Wang, et al.. (2023). Scene as Occupancy. The HKU Scholars Hub (University of Hong Kong). 8372–8381.47 indexed citations
Ge, Chongjian, et al.. (2021). Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning. Neural Information Processing Systems. 34.8 indexed citations
13.
Huo, Yuqi, Mingyu Ding, Haoyu Lu, et al.. (2021). Compressed Video Contrastive Learning. Neural Information Processing Systems. 34.3 indexed citations
14.
Zhang, Zhaoyang, Wenqi Shao, Jinwei Gu, Xiaogang Wang, & Ping Luo. (2021). Differentiable Dynamic Quantization with Mixed Precision and Adaptive Resolution. International Conference on Machine Learning. 12546–12556.1 indexed citations
15.
Luo, Ping, et al.. (2019). Differentiable Dynamic Normalization for Learning Deep Representation. International Conference on Machine Learning. 4203–4211.11 indexed citations
16.
Ao, Lin, et al.. (2019). A novel mutation panel for predicting etoposide resistance in small-cell lung cancer. SHILAP Revista de lepidopterología.4 indexed citations
17.
Luo, Ping, et al.. (2018). Differentiable Learning-to-Normalize via Switchable Normalization. International Conference on Learning Representations.12 indexed citations
Luo, Ping. (2017). Learning deep architectures via generalized Whitened Neural Networks. International Conference on Machine Learning. 2238–2246.16 indexed citations
20.
Zhao, Xiaoping, et al.. (2009). [MyoD mRNA expression in skeletal muscle of patients with myotonic dystrophy].. PubMed. 89(7). 466–8.1 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.