Chongjian Ge

1.4k total citations
9 papers, 350 citations indexed

About

Chongjian Ge is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Chongjian Ge has authored 9 papers receiving a total of 350 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 1 paper in Automotive Engineering. Recurrent topics in Chongjian Ge's work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chongjian Ge is often cited by papers focused on Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Generative Adversarial Networks and Image Synthesis (2 papers). Chongjian Ge collaborates with scholars based in Hong Kong, China and Sweden. Chongjian Ge's co-authors include Ping Luo, Yibing Song, Yuying Ge, Wei Liu, Enze Xie, Ruimao Zhang, Han Yang, Runjian Chen, Ding Liang and Shoufa Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

In The Last Decade

Chongjian Ge

9 papers receiving 336 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chongjian Ge Hong Kong	8	264	74	57	46	30	9	350
Alain Crouzil France	13	318 1.2×	69 0.9×	43 0.8×	54 1.2×	21 0.7×	28	379
Yunqi Lei China	11	213 0.8×	52 0.7×	74 1.3×	28 0.6×	11 0.4×	45	341
Armin Mustafa United Kingdom	6	219 0.8×	34 0.5×	40 0.7×	30 0.7×	46 1.5×	19	277
Hassan Abu Alhaija Germany	5	375 1.4×	87 1.2×	26 0.5×	31 0.7×	41 1.4×	7	445
Andrei Bursuc France	8	226 0.9×	95 1.3×	49 0.9×	10 0.2×	25 0.8×	31	393
Alessio Tonioni Italy	11	284 1.1×	84 1.1×	27 0.5×	28 0.6×	10 0.3×	18	391
Wen‐Kai Tai Taiwan	10	221 0.8×	34 0.5×	38 0.7×	65 1.4×	10 0.3×	40	337
Zizhao Wu China	9	234 0.9×	108 1.5×	192 3.4×	70 1.5×	6 0.2×	47	441
Shiyi Lan United States	5	208 0.8×	52 0.7×	100 1.8×	29 0.6×	10 0.3×	6	310

Countries citing papers authored by Chongjian Ge

Since Specialization

Citations

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

Fields of papers citing papers by Chongjian Ge

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chongjian Ge

This figure shows the co-authorship network connecting the top 25 collaborators of Chongjian Ge. A scholar is included among the top collaborators of Chongjian Ge 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 Chongjian Ge. Chongjian Ge 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.

Wang, Tianqi, Enze Xie, Chongjian Ge, et al.. (2024). DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving. Proceedings of the AAAI Conference on Artificial Intelligence. 38(6). 5599–5606. 35 indexed citations

2.

Ge, Chongjian, Yibing Song, Chao Ma, Yuankai Qi, & Ping Luo. (2023). Rethinking Attentive Object Detection via Neural Attention Learning. IEEE Transactions on Image Processing. 33. 1726–1739. 18 indexed citations

3.

Chen, Shoufa, Enze Xie, Chongjian Ge, et al.. (2023). CycleMLP: A MLP-Like Architecture for Dense Visual Predictions. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 14284–14300. 61 indexed citations

4.

Ge, Chongjian, Jun Song Chen, Enze Xie, et al.. (2023). MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation. 8687–8697. 26 indexed citations

5.

Ge, Chongjian, et al.. (2022). AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation. 36722–36732. 1 indexed citations

6.

Ge, Chongjian, et al.. (2021). Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning. Neural Information Processing Systems. 34. 8 indexed citations

7.

Sun, Peize, Enze Xie, Chongjian Ge, et al.. (2021). Watch Only Once: An End-to-End Video Action Detection Framework. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8158–8167. 35 indexed citations

8.

Ge, Yuying, Yibing Song, Ruimao Zhang, et al.. (2021). Parser-Free Virtual Try-on via Distilling Appearance Flows. 8481–8489. 102 indexed citations

9.

Ge, Chongjian, Yibing Song, Yuying Ge, et al.. (2021). Disentangled Cycle Consistency for Highly-realistic Virtual Try-On. 16923–16932. 64 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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