Song Tang
About
Song Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering.
According to data from OpenAlex, Song Tang has authored 38 papers receiving a total of 373 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 3 papers in Electrical and Electronic Engineering. Recurrent topics in Song Tang’s work include Domain Adaptation and Few-Shot Learning (14 papers), Multimodal Machine Learning Applications (11 papers) and Advanced Neural Network Applications (7 papers). Song Tang is often cited by papers focused on Domain Adaptation and Few-Shot Learning (14 papers), Multimodal Machine Learning Applications (11 papers) and Advanced Neural Network Applications (7 papers). Song Tang collaborates with scholars based in China, Germany and United Kingdom. Song Tang's co-authors include Mao Ye, Qingdu Li, Xudong Li, Lijuan Chen and Jianwei Zhang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Pattern Recognition.
In The Last Decade
Fields of papers citing papers by Song Tang
Since SpecializationEngineeringComputer SciencePhysics and AstronomyMathematicsEarth and Planetary SciencesEnergyEnvironmental ScienceMaterials ScienceChemical EngineeringChemistryAgricultural and Biological SciencesVeterinaryDecision SciencesArts and HumanitiesBusiness, Management and AccountingSocial SciencesPsychologyEconomics, Econometrics and FinanceHealth ProfessionsDentistryMedicineBiochemistry, Genetics and Molecular BiologyNeuroscienceNursingImmunology and MicrobiologyPharmacology, Toxicology and Pharmaceutics
This network shows the specialization of papers citing the papers produced by Song Tang. Nodes represent fields, and links connect fields that are likely to share authors. The network helps show where Song Tang may publish in the future.
Countries citing papers authored by Song Tang
Since SpecializationCitations
This map shows the geographic impact of Song Tang'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 Song Tang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Song Tang more than expected).
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