Chenxi Liu

3.5k total citations · 1 hit paper
12 papers, 916 citations indexed

About

Chenxi Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Control and Systems Engineering. According to data from OpenAlex, Chenxi Liu has authored 12 papers receiving a total of 916 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 3 papers in Artificial Intelligence and 2 papers in Control and Systems Engineering. Recurrent topics in Chenxi Liu's work include Advanced Neural Network Applications (4 papers), Advanced Measurement and Detection Methods (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Chenxi Liu is often cited by papers focused on Advanced Neural Network Applications (4 papers), Advanced Measurement and Detection Methods (2 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Chenxi Liu collaborates with scholars based in United States, China and United Kingdom. Chenxi Liu's co-authors include Alan Yuille, Wei Hua, Liang-Chieh Chen, Hartwig Adam, Li Fei-Fei, Florian Schroff, Shuicheng Yan, Xiaohui Shen, Zhe Lin and Xin Lu and has published in prestigious journals such as IEEE Transactions on Signal Processing, Energy and International Journal of Computer Vision.

In The Last Decade

Chenxi Liu

11 papers receiving 892 citations

Hit Papers

Auto-DeepLab: Hierarchical Neural Architecture Search for... 2019 2026 2021 2023 2019 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chenxi Liu United States 6 693 412 92 81 39 12 916
Fengxiang He China 16 572 0.8× 345 0.8× 122 1.3× 99 1.2× 30 0.8× 36 997
Gedas Bertasius United States 14 823 1.2× 261 0.6× 110 1.2× 46 0.6× 26 0.7× 35 1.0k
Pedro O. Pinheiro Canada 9 691 1.0× 366 0.9× 88 1.0× 57 0.7× 21 0.5× 13 968
Snehasis Mukherjee India 12 450 0.6× 283 0.7× 122 1.3× 91 1.1× 57 1.5× 43 900
Qiu Chen Japan 16 436 0.6× 316 0.8× 81 0.9× 55 0.7× 35 0.9× 97 821
Pengfei Xu China 16 678 1.0× 366 0.9× 76 0.8× 45 0.6× 46 1.2× 53 901
Samuel Dodge United States 7 380 0.5× 286 0.7× 48 0.5× 71 0.9× 21 0.5× 9 623
Xianxu Hou China 16 646 0.9× 297 0.7× 108 1.2× 102 1.3× 28 0.7× 38 959
Amit Agrawal United States 6 849 1.2× 343 0.8× 160 1.7× 79 1.0× 30 0.8× 14 1.1k
Hongyang Li China 14 806 1.2× 374 0.9× 90 1.0× 79 1.0× 37 0.9× 25 1.0k

Countries citing papers authored by Chenxi Liu

Since Specialization
Citations

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

Fields of papers citing papers by Chenxi Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenxi Liu

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

All Works

12 of 12 papers shown
1.
Hu, Xiaoling, et al.. (2025). Detection and Multiparameter Estimation for NLoS Targets: An RIS-Assisted Framework. IEEE Transactions on Signal Processing. 73. 1470–1484. 1 indexed citations
2.
Shen, X. Y., et al.. (2024). Research on truck mass estimation based on long short-term memory network. Energy. 307. 132729–132729. 3 indexed citations
3.
Chen, Wei, et al.. (2024). De-Diffusion Makes Text a Strong Cross-Modal Interface. 13492–13503. 3 indexed citations
4.
Liu, Chenxi. (2024). A Review of Digital Image Processing Techniques and Future Prospects. 4(3). 223–233. 1 indexed citations
5.
Xu, Zirui, Fuxun Yu, Chenxi Liu, et al.. (2022). FalCon: Fine-grained Feature Map Sparsity Computing with Decomposed Convolutions for Inference Optimization. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3634–3644. 6 indexed citations
6.
Jiang, Tao, et al.. (2020). A New Method for Extracting Laver Culture Carriers Based on Inaccurate Supervised Classification with FCN-CRF. Journal of Marine Science and Engineering. 8(4). 274–274. 10 indexed citations
7.
Yuille, Alan & Chenxi Liu. (2020). Deep Nets: What have They Ever Done for Vision?. International Journal of Computer Vision. 129(3). 781–802. 45 indexed citations
8.
Liu, Chenxi, Liang-Chieh Chen, Florian Schroff, et al.. (2019). Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation. 82–92. 628 indexed citations breakdown →
9.
Zhu, Zhuotun, Chenxi Liu, Dong Yang, Alan Yuille, & Daguang Xu. (2019). V-NAS: Neural Architecture Search for Volumetric Medical Image Segmentation. 240–248. 62 indexed citations
10.
Liu, Chenxi, Zhe Lin, Xiaohui Shen, et al.. (2017). Recurrent Multimodal Interaction for Referring Image Segmentation. 1280–1289. 155 indexed citations
11.
Liu, Chenxi, et al.. (2015). CEEMD and wavelet transform jointed de-noising method. Progress in geophysics. 30(6). 2870–2877.

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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