The diversity of canonical and ubiquitous progress in computer vision: A dynamic topic modeling approach |
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| Institution: | 1. Faculty of Economics and Management, East China Normal University, Shanghai, China;2. Key Laboratory of Advanced Theory and Application in Statistics and Data Science (East China Normal University), Ministry of Education of China, Shanghai, China;3. University of Chinese Academy of Sciences, Shanghai, China;1. Studio Galilei Co. Ltd., Yongin, Gyeonggi, Republic of Korea;2. Department of Transportation Engineering, College of Engineering, Myongji University, Yongin, Gyeonggi, Republic of Korea;3. Department of Geography, College of Sciences, Kyung Hee University, Seoul, Republic of Korea;4. Department of Transportation Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea;5. Smart Tourism Education Platform, College of Hotel & Tourism Management, Kyung Hee University, Seoul, Republic of Korea;6. Korea Railroad Research Institute, Uiwang, Gyeonggi, Republic of Korea;1. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, China;2. School of Economics and Management, Qilu University of Technology (Shandong Academy of Sciences), China;1. Research Institute for Smart Cities, School of Architecture and Urban Planning, Shenzhen University and Shenzhen Key Laboratory of Spatial Smart Sensing and Services and MNR Technology Innovation Center of Territorial and Spatial Big Data and Guangdong–Hong Kong-Macau Joint Laboratory for Smart Cities, Shenzhen 518060, China;2. Logistics Information Centre, Beijing 100842, China;3. Department of Game Design, Faculty of Arts, Uppsala University, Sweden |
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| Abstract: | Research trends are the keys for researchers to decide their research agenda. However, only a few works have tried to quantify how scholars follow the research trends. We address this question by proposing a novel measurement for quantifying how a scientific entity (paper or researcher) follows the hot topics in a research field. Based on extended dynamic topic modeling, the degree of hotness tracing of papers and scholars is explored from three perspectives: mainstream, short-term direction, and long-term direction. By analyzing a large-scale dataset containing more than 270,000 papers and 45,000 authors in Computer Vision (CV), we found that the authors’ orientation is more in the established mainstream rather than based on incremental directions and makes little difference in the choice of long-term or short-term direction. Moreover, we identified six groups of researchers in the CV community by clustering research behavior, who differed significantly in their patterns of orientation, topic selection, and impact. This study provides a new quantitative method for analyzing topic trends and scholars’ research interests, capturing the diversity of research behavior patterns to address the phenomenon of canonical and ubiquitous progress in research fields. |
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