Jingda Guo

799 total citations
13 papers, 538 citations indexed

About

Jingda Guo is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Jingda Guo has authored 13 papers receiving a total of 538 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Jingda Guo's work include Advanced Neural Network Applications (11 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Video Surveillance and Tracking Methods (4 papers). Jingda Guo is often cited by papers focused on Advanced Neural Network Applications (11 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Video Surveillance and Tracking Methods (4 papers). Jingda Guo collaborates with scholars based in United States and China. Jingda Guo's co-authors include Qing Yang, Song Fu, Sihai Tang, Qi Chen, Xu Ma, Jinbo Xiong, Renwan Bi, Qing Yang, Qi Chen and Paparao Palacharla and has published in prestigious journals such as IEEE Internet of Things Journal, IEEE Wireless Communications and Ceramics International.

In The Last Decade

Jingda Guo

13 papers receiving 531 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jingda Guo United States	8	276	156	125	108	81	13	538
Sihai Tang United States	9	393 1.4×	161 1.0×	204 1.6×	152 1.4×	129 1.6×	17	704
Abhishek Gupta Canada	9	199 0.7×	95 0.6×	107 0.9×	94 0.9×	54 0.7×	22	498
Young‐Woo Seo United States	12	331 1.2×	141 0.9×	209 1.7×	84 0.8×	43 0.5×	28	681
Qieshi Zhang China	14	423 1.5×	156 1.0×	43 0.3×	119 1.1×	63 0.8×	91	685
Ye Shi China	14	153 0.6×	168 1.1×	115 0.9×	280 2.6×	54 0.7×	42	731
Zhe Xuanyuan China	9	281 1.0×	116 0.7×	241 1.9×	66 0.6×	47 0.6×	20	659
Jiangbo Qian China	13	207 0.8×	180 1.2×	34 0.3×	73 0.7×	92 1.1×	97	573
Hang Shi China	11	211 0.8×	101 0.6×	36 0.3×	60 0.6×	80 1.0×	40	439
Manato Hirabayashi Japan	6	195 0.7×	63 0.4×	196 1.6×	81 0.8×	70 0.9×	16	496

Countries citing papers authored by Jingda Guo

Since Specialization

Citations

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

Fields of papers citing papers by Jingda Guo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jingda Guo

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

13 of 13 papers shown

1.

Guo, Jingda, Qi Chen, Qing Yang, et al.. (2022). Slim-FCP: Lightweight-Feature-Based Cooperative Perception for Connected Automated Vehicles. IEEE Internet of Things Journal. 9(17). 15630–15638. 19 indexed citations

2.

Ma, Xu, Jingda Guo, Qi Chen, et al.. (2021). Spatial Pyramid Attention for Deep Convolutional Neural Networks. IEEE Transactions on Multimedia. 23. 3048–3058. 32 indexed citations

3.

Guo, Jingda, Sihai Tang, Qi Chen, et al.. (2021). CoFF: Cooperative Spatial Feature Fusion for 3-D Object Detection on Autonomous Vehicles. IEEE Internet of Things Journal. 8(14). 11078–11087. 42 indexed citations

4.

Sun, Zheng, et al.. (2021). Effects of 0.5B2O3–0.5CuO on the microwave dielectric properties of low-temperature sintered ZZNT ceramics. Ceramics International. 48(5). 7153–7158. 3 indexed citations

5.

Ma, Xu, Jingda Guo, Sihai Tang, et al.. (2021). Learning Connected Attentions for Convolutional Neural Networks. 1–6. 18 indexed citations

6.

Guo, Jingda, Xu Ma, Qi Chen, et al.. (2020). Spanet: Spatial Pyramid Attention Network for Enhanced Image Recognition. 1–6. 61 indexed citations

7.

Ma, Xu, Jingda Guo, Qi Chen, et al.. (2020). Attention Meets Normalization and Beyond. 1–6. 2 indexed citations

8.

Xiong, Jinbo, et al.. (2020). Edge-Assisted Privacy-Preserving Raw Data Sharing Framework for Connected Autonomous Vehicles. IEEE Wireless Communications. 27(3). 24–30. 99 indexed citations

9.

Ma, Xu, Jingda Guo, Sihai Tang, et al.. (2020). Cascaded Context Dependency: An Extremely Lightweight Module For Deep Convolutional Neural Networks. 1741–1745. 1 indexed citations

10.

Guo, Jingda, et al.. (2020). Towards Trustworthy Perception Information Sharing on Connected and Autonomous Vehicles. 85–90. 3 indexed citations

11.

Chen, Qi, Jingda Guo, Yuan Li, et al.. (2019). Low-Latency High-Level Data Sharing for Connected and Autonomous Vehicular Networks. 287–296. 9 indexed citations

12.

Guo, Jingda, et al.. (2019). Detection of Occluded Road Signs on Autonomous Driving Vehicles. 856–861. 5 indexed citations

13.

Chen, Qi, Xu Ma, Sihai Tang, et al.. (2019). F-cooper. 88–100. 244 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.

Explore authors with similar magnitude of impact