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.
Contrastive Adaptation Network for Unsupervised Domain Adaptation
2019598 citationsGuoliang Kang, Lu Jiang et al.profile →
Self-Paced Curriculum Learning
2015309 citationsLu Jiang, Deyu Meng et al.Proceedings of the AAAI Conference on Artificial Intelligenceprofile →
MaskGIT: Masked Generative Image Transformer
2022167 citationsHuiwen Chang, Han Zhang et al.2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Lu Jiang'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 Lu Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lu Jiang more than expected).
This network shows the impact of papers produced by Lu Jiang. 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 Lu Jiang. The network helps show where Lu Jiang may publish in the future.
Co-authorship network of co-authors of Lu Jiang
This figure shows the co-authorship network connecting the top 25 collaborators of Lu Jiang.
A scholar is included among the top collaborators of Lu Jiang 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 Lu Jiang. Lu Jiang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Chang, Huiwen, Han Zhang, Lu Jiang, Ce Liu, & William T. Freeman. (2022). MaskGIT: Masked Generative Image Transformer. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 11305–11315.167 indexed citations breakdown →
Wang, Yunbo, et al.. (2019). Eidetic 3D LSTM: A Model for Video Prediction and Beyond. International Conference on Learning Representations.111 indexed citations
Jiang, Lu, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, & Li Fei-Fei. (2018). MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels. International Conference on Machine Learning. 2304–2313.181 indexed citations
12.
Jiang, Lu, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, & Li Fei-Fei. (2017). MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels. arXiv (Cornell University).21 indexed citations
Liang, Junwei, Lu Jiang, Deyu Meng, & Alexander G. Hauptmann. (2016). Learning to detect concepts from webly-labeled video data. International Joint Conference on Artificial Intelligence. 1746–1752.24 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.