Shell Xu Hu

1.1k total citations
9 papers, 186 citations indexed

About

Shell Xu Hu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Industrial and Manufacturing Engineering. According to data from OpenAlex, Shell Xu Hu has authored 9 papers receiving a total of 186 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 5 papers in Computer Vision and Pattern Recognition and 2 papers in Industrial and Manufacturing Engineering. Recurrent topics in Shell Xu Hu's work include Advanced Neural Network Applications (4 papers), Machine Learning and Data Classification (3 papers) and Speech and dialogue systems (2 papers). Shell Xu Hu is often cited by papers focused on Advanced Neural Network Applications (4 papers), Machine Learning and Data Classification (3 papers) and Speech and dialogue systems (2 papers). Shell Xu Hu collaborates with scholars based in United Kingdom, France and Belgium. Shell Xu Hu's co-authors include Minyoung Kim, Timothy M. Hospedales, Da Li, Jan Stühmer, Xi Shen, Johan A. K. Suykens, Sergey Zagoruyko, Nikos Komodakis, Chunrong Ai and Yang Xiao and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, Computer Vision and Image Understanding and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

In The Last Decade

Shell Xu Hu

8 papers receiving 182 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Shell Xu Hu United Kingdom	6	125	86	22	16	11	9	186
Yiru Wang China	7	155 1.2×	110 1.3×	19 0.9×	18 1.1×	4 0.4×	16	238
Jafar Tahmoresnezhad Iran	10	232 1.9×	104 1.2×	11 0.5×	18 1.1×	8 0.7×	30	301
Jiequan Cui Hong Kong	6	254 2.0×	176 2.0×	50 2.3×	21 1.3×	6 0.5×	8	356
Limin Su China	5	226 1.8×	135 1.6×	23 1.0×	15 0.9×	6 0.5×	13	293
Yu-Ting Chang United States	5	144 1.2×	241 2.8×	17 0.8×	18 1.1×	9 0.8×	7	305
Bingke Zhu China	8	114 0.9×	218 2.5×	11 0.5×	21 1.3×	18 1.6×	14	320
Hyeonwoo Noh South Korea	7	92 0.7×	160 1.9×	15 0.7×	5 0.3×	5 0.5×	9	221
Fabio Cermelli Italy	8	237 1.9×	222 2.6×	54 2.5×	21 1.3×	7 0.6×	13	323
Ajoy Mondal India	11	56 0.4×	203 2.4×	15 0.7×	48 3.0×	8 0.7×	26	258
Kean Chen China	6	95 0.8×	149 1.7×	6 0.3×	17 1.1×	7 0.6×	8	229

Countries citing papers authored by Shell Xu Hu

Since Specialization

Citations

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

Fields of papers citing papers by Shell Xu Hu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shell Xu Hu. 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 Shell Xu Hu. The network helps show where Shell Xu Hu may publish in the future.

Co-authorship network of co-authors of Shell Xu Hu

This figure shows the co-authorship network connecting the top 25 collaborators of Shell Xu Hu. A scholar is included among the top collaborators of Shell Xu Hu 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 Shell Xu Hu. Shell Xu Hu 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:

9 of 9 papers shown

1.

Hu, Shell Xu, et al.. (2024). Strong Baselines for Parameter-Efficient Few-Shot Fine-Tuning. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11024–11031. 5 indexed citations

2.

Hu, Shell Xu, et al.. (2024). Distilling Knowledge from Text-to-Image Generative Models Improves Visio-Linguistic Reasoning in CLIP. 6105–6113. 1 indexed citations

3.

Tan, Fuwen, Łukasz Dudziak, Shell Xu Hu, et al.. (2024). MobileQuant: Mobile-friendly Quantization for On-device Language Models. Edinburgh Research Explorer (University of Edinburgh). 9761–9771. 2 indexed citations

4.

Fan, Hongxiang, et al.. (2024). CLUES: Collaborative Private-domain High-quality Data Selection for LLMs via Training Dynamics. 141183–141207.

5.

Hu, Shell Xu, Da Li, Jan Stühmer, Minyoung Kim, & Timothy M. Hospedales. (2022). Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Difference. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9058–9067. 100 indexed citations

6.

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

7.

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

8.

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

9.

Hu, Shell Xu, Sergey Zagoruyko, & Nikos Komodakis. (2019). Exploring weight symmetry in deep neural networks. Computer Vision and Image Understanding. 187. 102786–102786. 15 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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