Shi Guo

2.8k total citations · 1 hit paper
9 papers, 499 citations indexed

About

Shi Guo is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Signal Processing. According to data from OpenAlex, Shi Guo has authored 9 papers receiving a total of 499 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 2 papers in Media Technology and 1 paper in Signal Processing. Recurrent topics in Shi Guo's work include Advanced Image Processing Techniques (7 papers), Advanced Vision and Imaging (6 papers) and Image Enhancement Techniques (4 papers). Shi Guo is often cited by papers focused on Advanced Image Processing Techniques (7 papers), Advanced Vision and Imaging (6 papers) and Image Enhancement Techniques (4 papers). Shi Guo collaborates with scholars based in Hong Kong, China and Cayman Islands. Shi Guo's co-authors include Lei Zhang, Gaofeng Ren, Wenyu Liu, Runsheng Yu, Jianke Zhu, Yunjin Chen, Wangmeng Zuo, Shuhang Gu, Kenneth K. Wong and Zhetong Liang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Shi Guo

9 papers receiving 486 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.

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

2022 309 citations Wenyu Liu, Gaofeng Ren et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers

Shi Guo

CVPR

MT

AE

AI

SRRQ

Huajun Zhou China

Zongyang Zhao China

Takumi Karasawa Japan

Cong Geng China

Yancheng Bai China

Liqi Yan China

C S Asha India

Rob G. J. Wijnhoven Netherlands

Dae-Seong Kang South Korea

Huajun Zhou China

Shi Guo

521 ×1.3

CVPR

90 ×0.6

MT

63 ×1.2

AE

48 ×1.5

AI

12 ×0.4

SRRQ

Citations per year, relative to Shi Guo Shi Guo (= 1×) peers Huajun Zhou

Countries citing papers authored by Shi Guo

Since Specialization

Citations

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

Fields of papers citing papers by Shi Guo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shi Guo

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

9 of 9 papers shown

1.

Li, Ruihuang, et al.. (2024). TMP: Temporal Motion Propagation for Online Video Super-Resolution. IEEE Transactions on Image Processing. 33. 5014–5028. 2 indexed citations

2.

Guo, Shi, et al.. (2023). Text Prior Guided Scene Text Image Super-Resolution. IEEE Transactions on Image Processing. 32. 1341–1353. 42 indexed citations

3.

Liu, Wenyu, Gaofeng Ren, Runsheng Yu, et al.. (2022). Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions. Proceedings of the AAAI Conference on Artificial Intelligence. 36(2). 1792–1800. 309 indexed citations breakdown →

4.

Guo, Shi, et al.. (2022). A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 17451–17460. 7 indexed citations

5.

Chen, Guanying, Chaofeng Chen, Shi Guo, et al.. (2021). HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 2482–2491. 37 indexed citations

6.

Qiao, Xiaotian, et al.. (2021). Distilling Reflection Dynamics for Single-Image Reflection Removal. 1886–1894. 3 indexed citations

7.

Gu, Shuhang, Shi Guo, Wangmeng Zuo, et al.. (2019). Learned Dynamic Guidance for Depth Image Reconstruction. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(10). 2437–2452. 21 indexed citations

8.

Yan, Zifei, et al.. (2019). On Combining CNN With Non-Local Self-Similarity Based Image Denoising Methods. IEEE Access. 8. 14789–14797. 10 indexed citations

9.

Gu, Shuhang, et al.. (2017). Learning Dynamic Guidance for Depth Image Enhancement. PolyU Institutional Research Archive (Hong Kong Polytechnic University). 712–721. 68 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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