Ge Song

56 total papers · 616 total citations
36 papers, 443 citations indexed

About

Ge Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Ge Song has authored 36 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 5 papers in Molecular Biology. Recurrent topics in Ge Song's work include Advanced Image and Video Retrieval Techniques (13 papers), Multimodal Machine Learning Applications (11 papers) and Domain Adaptation and Few-Shot Learning (7 papers). Ge Song is often cited by papers focused on Advanced Image and Video Retrieval Techniques (13 papers), Multimodal Machine Learning Applications (11 papers) and Domain Adaptation and Few-Shot Learning (7 papers). Ge Song collaborates with scholars based in China, United States and Singapore. Ge Song's co-authors include Xiaoyang Tan, Min Chen, Guoliang Fan, Guo‐Qiang Chen, Dong Wang, Jingyu Chen, Meng Ding, Dongmei Zhang, Ming Yang and Yingnong Dang and has published in prestigious journals such as Applied Microbiology and Biotechnology, Pattern Recognition and Molecular Physics.

In The Last Decade

Ge Song

33 papers receiving 428 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Ge Song	211	79	63	54	53	36	443
Yu Gai	88 0.4×	182 2.3×	6 0.1×	54 1.0×	33 0.6×	19	416
Xiaobin Li	104 0.5×	141 1.8×	23 0.4×	14 0.3×	25 0.5×	28	368
Prashant Sharma	84 0.4×	112 1.4×	57 0.9×	59 1.1×	51 1.0×	35	413
Basant Tiwari	56 0.3×	115 1.5×	29 0.5×	40 0.7×	55 1.0×	39	480
Chun Shan	65 0.3×	68 0.9×	13 0.2×	97 1.8×	43 0.8×	47	432
Hao Zhang	79 0.4×	21 0.3×	10 0.2×	79 1.5×	8 0.2×	42	434
Young-Won Kim	182 0.9×	77 1.0×	40 0.6×	76 1.4×	37 0.7×	45	461
Chongyu Liu	226 1.1×	64 0.8×	17 0.3×	80 1.5×	13 0.2×	35	403
Yingjie Wang	250 1.2×	48 0.6×	9 0.1×	43 0.8×	15 0.3×	39	475
Shaohua Li	93 0.4×	86 1.1×	9 0.1×	16 0.3×	23 0.4×	31	419

Countries citing papers authored by Ge Song

Since Specialization

Citations

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

Fields of papers citing papers by Ge Song

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ge Song

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

All Works

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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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