Jack Sim
Impact in
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- Advanced Image and Video Retrieval Techniques
- Video Surveillance and Tracking Methods
- Image Retrieval and Classification Techniques
- Advanced Neural Network Applications
- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
Papers in
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- Advanced Image and Video Retrieval Techniques 6
- Video Surveillance and Tracking Methods 3
- Multimodal Machine Learning Applications 3
- Image Retrieval and Classification Techniques 3
- Advanced Neural Network Applications 1
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- Domain Adaptation and Few-Shot Learning 2
- Co-authors
- André Araujo (4 shared papers)Tobias Weyand (2 shared papers)Bohyung Han (4 shared papers)Bingyi Cao (2 shared papers)Hartwig Adam (2 shared papers)Menglong Zhu (1 shared paper)Marvin Teichmann (1 shared paper)Haifeng Gong (1 shared paper)
- Journals
- Journal of Electronic Commerce in Organizations (1 paper)Apollo (University of Cambridge) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesUnited KingdomSouth Korea
In The Last Decade
Jack Sim
12 papers receiving 694 citations
Peers
Comparison fields: 5 of 102
- Computer Vision and Pattern Recognition 480
- Artificial Intelligence 158
- Media Technology 40
- Aerospace Engineering 92
- Signal Processing 39
Countries citing papers authored by Jack Sim
This map shows the geographic impact of Jack Sim'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 Jack Sim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jack Sim more than expected).
Fields of papers citing papers by Jack Sim
This network shows the impact of papers produced by Jack Sim. 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 Jack Sim. The network helps show where Jack Sim may publish in the future.
Co-authors
The 17 scholars most cited alongside Jack Sim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 192 | |
| 2 | 2020 | 168 | |
| 3 | 2017 | 100 | |
| 4 | 2019 | 76 | |
| 5 | 2021 | 70 | |
| 6 | 2011 | 56 | |
| 7 | 2011 | 32 | |
| 8 | Image Retrieval with Deep Local Features and Attention-based Keypoints. | 2016 | 6 |
| 9 | Unifying Deep Local and Global Features for Efficient Image Search. | 2020 | 5 |
| 10 | 2019 | 3 | |
| 11 | BranchOut: Regularization for Online Ensemble Tracking with CNNs | 2017 | 1 |
| 12 | 2010 | 1 |
About Jack Sim
Jack Sim is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Business and International Management and Signal Processing, having authored 12 papers that have together received 710 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (6 papers), Video Surveillance and Tracking Methods (3 papers), Multimodal Machine Learning Applications (3 papers), Image Retrieval and Classification Techniques (3 papers), Domain Adaptation and Few-Shot Learning (2 papers), Innovation and Socioeconomic Development (1 paper), Advanced Chemical Sensor Technologies (1 paper) and Advanced Neural Network Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (480 citations), Artificial Intelligence (158 citations), Media Technology (40 citations), Aerospace Engineering (92 citations) and Signal Processing (39 citations). Jack Sim has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include André Araujo, Tobias Weyand, Bohyung Han, Bingyi Cao, Hartwig Adam, Menglong Zhu, Marvin Teichmann, Haifeng Gong, Maxim Likhachev and Jianbo Shi. Their work appears in journals such as Journal of Electronic Commerce in Organizations, Apollo (University of Cambridge) and arXiv (Cornell University).
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.