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.
Generative Image Inpainting with Contextual Attention
20181.5k citationsZhe Lin, Shuicheng Yan et al.profile →
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
20211.4k citationsYifan Jiang, Xinyu Gong et al.IEEE Transactions on Image Processingprofile →
A convolutional neural network cascade for face detection
2015861 citationsHaoxiang Li, Zhe Lin et al.profile →
A unified approach to salient object detection via low rank matrix recovery
This map shows the geographic impact of Xiaohui Shen'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 Xiaohui Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaohui Shen more than expected).
This network shows the impact of papers produced by Xiaohui Shen. 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 Xiaohui Shen. The network helps show where Xiaohui Shen may publish in the future.
Co-authorship network of co-authors of Xiaohui Shen
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaohui Shen.
A scholar is included among the top collaborators of Xiaohui Shen 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 Xiaohui Shen. Xiaohui Shen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Jin, Xiaojie, Huaxin Xiao, Xiaohui Shen, et al.. (2017). Predicting Scene Parsing and Motion Dynamics in the Future. Neural Information Processing Systems. 30. 6915–6924.18 indexed citations
Seo, Paul Hongsuck, Zhe Lin, Scott Cohen, Xiaohui Shen, & Bohyung Han. (2016). Progressive Attention Networks for Visual Attribute Prediction. Seoul National University Open Repository (Seoul National University). 112.3 indexed citations
Li, Haoxiang, Zhe Lin, Xiaohui Shen, Jonathan Brandt, & Gang Hua. (2015). A convolutional neural network cascade for face detection. 5325–5334.861 indexed citations breakdown →
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.