Xi Shen

1.5k total citations · 1 hit paper
19 papers, 672 citations indexed

About

Xi Shen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Xi Shen has authored 19 papers receiving a total of 672 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 2 papers in Media Technology. Recurrent topics in Xi Shen's work include Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Visual Attention and Saliency Detection (3 papers). Xi Shen is often cited by papers focused on Advanced Neural Network Applications (6 papers), Advanced Image and Video Retrieval Techniques (6 papers) and Visual Attention and Saliency Detection (3 papers). Xi Shen collaborates with scholars based in France, China and United States. Xi Shen's co-authors include Tie‐Qiang Li, Yongxia Zhou, Xiuhui Wang, Yuan Yuan, Chi‐Man Pun, Weihuang Liu, Xiaodong Cun, Dominique Vaufreydaz, James L. Crowley and Lang Rao and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Nature Nanotechnology.

In The Last Decade

Xi Shen

19 papers receiving 652 citations

Hit Papers

align trajectories

What are hit papers?

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.

Designing nanotheranostics with machine learning

2024 91 citations Lang Rao, Yuan Yuan et al. Nature Nanotechnology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Xi Shen France	12	363	307	119	97	44	19	672
Feng Yang China	15	249 0.7×	253 0.8×	73 0.6×	102 1.1×	26 0.6×	87	796
Chris G. Willcocks United Kingdom	9	319 0.9×	232 0.8×	153 1.3×	131 1.4×	28 0.6×	20	627
Lingqiao Li China	14	399 1.1×	266 0.9×	153 1.3×	63 0.6×	118 2.7×	44	678
Nouf Abdullah Almujally Saudi Arabia	14	237 0.7×	166 0.5×	80 0.7×	69 0.7×	25 0.6×	65	599
Tianyu Shen China	12	163 0.4×	197 0.6×	104 0.9×	36 0.4×	20 0.5×	45	510
Walid Barhoumi Tunisia	18	460 1.3×	414 1.3×	210 1.8×	75 0.8×	47 1.1×	99	965
Chengming Xu China	10	464 1.3×	479 1.6×	74 0.6×	57 0.6×	34 0.8×	25	754
Huan Xiong China	12	386 1.1×	233 0.8×	54 0.5×	26 0.3×	45 1.0×	57	664

Countries citing papers authored by Xi Shen

Since Specialization

Citations

This map shows the geographic impact of Xi 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 Xi Shen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xi Shen more than expected).

Fields of papers citing papers by Xi Shen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xi 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 Xi Shen. The network helps show where Xi Shen may publish in the future.

Co-authorship network of co-authors of Xi Shen

This figure shows the co-authorship network connecting the top 25 collaborators of Xi Shen. A scholar is included among the top collaborators of Xi 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 Xi Shen. Xi Shen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

19 of 19 papers shown

Chen, Dexiong, et al.. (2024). HTR-VT: Handwritten text recognition with vision transformer. Pattern Recognition. 158. 110967–110967. 12 indexed citations

Rao, Lang, Yuan Yuan, Xi Shen, Guocan Yu, & Xiaoyuan Chen. (2024). Designing nanotheranostics with machine learning. Nature Nanotechnology. 19(12). 1769–1781. 91 indexed citations breakdown →

Chen, Dexiong, et al.. (2024). SURE: SUrvey REcipes for Building Reliable and Robust Deep Networks. 17500–17510. 3 indexed citations

Shen, Xi, Yuan Yuan, Yuming Du, et al.. (2023). TokenCut: Segmenting Objects in Images and Videos With Self-Supervised Transformer and Normalized Cut. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 15790–15801. 33 indexed citations

Liu, Weihuang, Xi Shen, Chi‐Man Pun, & Xiaodong Cun. (2023). Explicit Visual Prompting for Low-Level Structure Segmentations. 19434–19445. 83 indexed citations

Guo, W., Yuming Du, Xi Shen, et al.. (2023). Back to MLP: A Simple Baseline for Human Motion Prediction. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4798–4808. 76 indexed citations

Shen, Xi, et al.. (2022). Self-Supervised Transformers for Unsupervised Object Discovery using Normalized Cut. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 14523–14533. 88 indexed citations

Shen, Xi, Shiry Ginosar, M Rousselot, et al.. (2022). Spatially-Consistent Feature Matching and Learning for Heritage Image Analysis. International Journal of Computer Vision. 130(5). 1325–1339. 3 indexed citations

Hu, Shell Xu, et al.. (2022). Compressing Features for Learning With Noisy Labels. IEEE Transactions on Neural Networks and Learning Systems. 35(2). 2124–2138. 21 indexed citations

10.

Shen, Xi, Alexei A. Efros, Armand Joulin, & Mathieu Aubry. (2022). Learning Co-segmentation by Segment Swapping for Retrieval and Discovery. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 5078–5088. 6 indexed citations

11.

Xu, Zhenhao, et al.. (2021). Detection of Cervical Cancer Cells in Whole Slide Images Using Deformable and Global Context Aware Faster RCNN-FPN. Current Oncology. 28(5). 3585–3601. 53 indexed citations

12.

Wu, Zhenyang, et al.. (2021). Bearing fault diagnosis method based on EMD-VMD adjacent reconstruction and secondary decomposition. 33. 6785–6790. 4 indexed citations

13.

Shen, Xi, et al.. (2021). Re-ranking for image retrieval and transductive few-shot classification. HAL (Le Centre pour la Communication Scientifique Directe). 16 indexed citations

14.

Shen, Xi, et al.. (2021). Boosting Co-teaching with Compression Regularization for Label Noise. 2682–2686. 26 indexed citations

15.

Shen, Xi, et al.. (2021). Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach. 6810–6817. 2 indexed citations

16.

Xu, Hu, et al.. (2020). Empirical Bayes Transductive Meta-Learning with Synthetic Gradients. International Conference on Learning Representations. 13 indexed citations

17.

Shen, Xi, et al.. (2020). Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PLoS ONE. 15(5). e0232127–e0232127. 130 indexed citations

18.

Shen, Xi & Ronaldo Messina. (2016). A Method of Synthesizing Handwritten Chinese Images for Data Augmentation. 114–119. 10 indexed citations

19.

Shen, Xi, et al.. (2015). Spatial-temporal correlation for trajectory based action video retrieval. 41. 1–6. 2 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.

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