Boxi Wu

1.7k total citations · 1 hit paper
42 papers, 477 citations indexed

About

Boxi Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Condensed Matter Physics. According to data from OpenAlex, Boxi Wu has authored 42 papers receiving a total of 477 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 11 papers in Computer Vision and Pattern Recognition and 6 papers in Condensed Matter Physics. Recurrent topics in Boxi Wu's work include Neural Networks and Applications (13 papers), Advanced Neural Network Applications (6 papers) and GaN-based semiconductor devices and materials (5 papers). Boxi Wu is often cited by papers focused on Neural Networks and Applications (13 papers), Advanced Neural Network Applications (6 papers) and GaN-based semiconductor devices and materials (5 papers). Boxi Wu collaborates with scholars based in China, Hong Kong and United States. Boxi Wu's co-authors include Xiaofei He, Binbin Lin, Long Chen, Wei Liu, Wei Chen, Wenxiao Wang, Jianwei Shuai, Haifeng Liu, Ke Li and Minghao Chen and has published in prestigious journals such as Advanced Materials, Physical review. B, Condensed matter and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Boxi Wu

41 papers receiving 458 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.

CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention

2023 157 citations Wei Chen, Long Chen et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Boxi Wu China	10	232	147	57	45	40	42	477
S. Susan Young United States	11	197 0.8×	84 0.6×	76 1.3×	61 1.4×	79 2.0×	34	381
Stephen T. Welstead United States	7	134 0.6×	87 0.6×	42 0.7×	23 0.5×	36 0.9×	16	391
Muhammad Asif Khan Pakistan	13	231 1.0×	133 0.9×	59 1.0×	56 1.2×	29 0.7×	39	436
Jiun-Wei Liou Taiwan	5	120 0.5×	131 0.9×	40 0.7×	17 0.4×	56 1.4×	8	432
J.‐G. Postaire France	14	396 1.7×	157 1.1×	28 0.5×	47 1.0×	133 3.3×	73	609
Jitesh Jain India	9	229 1.0×	73 0.5×	73 1.3×	40 0.9×	35 0.9×	21	417
Chuang Wang China	13	162 0.7×	328 2.2×	56 1.0×	56 1.2×	16 0.4×	38	580
Zhenhua Li China	13	125 0.5×	270 1.8×	36 0.6×	41 0.9×	46 1.1×	42	492
Biao Gong China	4	289 1.2×	106 0.7×	74 1.3×	28 0.6×	44 1.1×	10	504
Ramakrishna Kakarala Singapore	13	359 1.5×	49 0.3×	39 0.7×	26 0.6×	97 2.4×	56	549

Countries citing papers authored by Boxi Wu

Since Specialization

Citations

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

Fields of papers citing papers by Boxi Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Boxi Wu

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

20 of 20 papers shown

1.

Wen, Wang, Liang Peng, Qinglin Lu, et al.. (2025). LoRA-Composer: Leveraging Low-Rank Adaptation for Multi-Concept Customization in Training-Free Diffusion Models. IEEE Transactions on Image Processing. 34. 8145–8158. 2 indexed citations

2.

Wu, Boxi, Cheng Wu, Chenglei Pei, et al.. (2024). Long-term hourly air quality data bridging of neighboring sites using automated machine learning: A case study in the Greater Bay area of China. Atmospheric Environment. 321. 120347–120347. 7 indexed citations

3.

Peng, Liang, et al.. (2024). Temporal Feature Fusion for 3D Detection in Monocular Video. IEEE Transactions on Image Processing. 33. 2665–2675. 1 indexed citations

4.

Li, Mingming, et al.. (2024). Towards Fine-Grained HBOE with Rendered Orientation Set and Laplace Smoothing. Proceedings of the AAAI Conference on Artificial Intelligence. 38(7). 7505–7513. 2 indexed citations

5.

Peng, Liang, Yang Zheng, Xiaopei Wu, et al.. (2024). Learning Occupancy for Monocular 3D Object Detection. 10281–10292. 9 indexed citations

6.

Chen, Minghao, Shuai Xiao, Liang Peng, et al.. (2024). Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-Dataset 3D Object Detection. 15291–15300. 6 indexed citations

7.

Chen, Wei, Long Chen, Boxi Wu, et al.. (2023). CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(5). 3123–3136. 157 indexed citations breakdown →

8.

Wu, Boxi, Jie Jiang, Wenxiao Wang, et al.. (2023). Towards In-Distribution Compatible Out-of-Distribution Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 37(9). 10333–10341. 1 indexed citations

9.

Zhong, Chen, et al.. (2004). Study on phase-space reconstruction of chaotic signal based on wavelet transform. Acta Physica Sinica. 53(9). 2882–2882. 11 indexed citations

10.

Guo, Donghui, et al.. (2004). Method of generating statistical rice random variables over wireless fading channels. 1. 165–168. 1 indexed citations

11.

Wu, Boxi, et al.. (2003). Pattern grouping strategy makes BP algorithm less sensitive to learning rate. 3. 1753–1756. 1 indexed citations

12.

Dong, Jiyang, et al.. (2001). On permutation symmetries of hopfield model neural network. Discrete Dynamics in Nature and Society. 6(2). 129–136. 3 indexed citations

13.

Wu, Boxi, et al.. (2000). [Matching of brain sections based on fourier descriptors].. PubMed. 17(4). 437–9. 1 indexed citations

14.

Shuai, Jianwei, et al.. (1997). A simple way to determine the connecting of a multilinear neural network. Physics Letters A. 227(1-2). 94–96. 2 indexed citations

15.

Shuai, Jianwei, et al.. (1996). A Chaotic Neural Network Model. Chinese Physics Letters. 13(3). 185–188. 3 indexed citations

16.

Shuai, Jianwei, et al.. (1996). Self-evolution neural model. Physics Letters A. 221(5). 311–316. 13 indexed citations

17.

Chen, Zhenxiang, et al.. (1996). The storage capacity of the complex phasor neural network. Physica A Statistical Mechanics and its Applications. 225(2). 157–163. 3 indexed citations

18.

Shuai, Jianwei, et al.. (1995). The Hamilton neural network model. Physica A Statistical Mechanics and its Applications. 216(1-2). 20–31. 5 indexed citations

19.

Shuai, Jianwei, et al.. (1995). The stability of the 2n-element number neural network models. Physica A Statistical Mechanics and its Applications. 218(3-4). 291–297. 2 indexed citations

20.

Wu, Boxi, et al.. (1984). Electromodulation reflection method for study of the forming process in DC electroluminescent ZnS:Mn, CU. Journal of Luminescence. 31-32. 951–953.

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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