Johan Björck
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Human Pose and Action Recognition
Papers in
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- Machine Learning in Materials Science 3
- Electronic and Structural Properties of Oxides 2
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- Catalysis and Oxidation Reactions 3
- Co-authors
- Kriti Aggarwal (1 shared paper)Owais Khan Mohammed (1 shared paper)Furu Wei (1 shared paper)Wenhui Wang (1 shared paper)Subhojit Som (1 shared paper)Saksham Singhal (1 shared paper)Dong Li (1 shared paper)Hangbo Bao (1 shared paper)
- Journals
- Applied Physics Letters (1 paper)MRS Communications (1 paper)ACS Combinatorial Science (1 paper)AI Magazine (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesChinaFinland
In The Last Decade
Johan Björck
12 papers receiving 447 citations
Johan Björck's Hit Papers
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 206
- Developmental Biology 15
- Artificial Intelligence 168
- Media Technology 19
- Signal Processing 22
Countries citing papers authored by Johan Björck
This map shows the geographic impact of Johan Björck'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 Johan Björck with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johan Björck more than expected).
Fields of papers citing papers by Johan Björck
This network shows the impact of papers produced by Johan Björck. 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 Johan Björck. The network helps show where Johan Björck may publish in the future.
Co-authors
The 25 scholars most cited alongside Johan Björck, 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 | Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasks Hit paper breakdown → | 2023 | 260 |
| 2 | 2016 | 62 | |
| 3 | 2017 | 35 | |
| 4 | 2019 | 27 | |
| 5 | 2019 | 21 | |
| 6 | 2018 | 16 | |
| 7 | 2021 | 15 | |
| 8 | 2021 | 7 | |
| 9 | 2017 | 6 | |
| 10 | Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision | 2021 | 4 |
| 11 | 2018 | 3 | |
| 12 | 2021 | 1 |
About Johan Björck
Johan Björck is a scholar working on Materials Chemistry, Catalysis, Computer Vision and Pattern Recognition, Ecology and Artificial Intelligence, having authored 12 papers that have together received 457 indexed citations. Recurring topics across this work include Catalysis and Oxidation Reactions (3 papers), Machine Learning in Materials Science (3 papers), Electronic and Structural Properties of Oxides (2 papers), Wildlife Ecology and Conservation (1 paper), Transition Metal Oxide Nanomaterials (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Electron and X-Ray Spectroscopy Techniques (1 paper) and Human Pose and Action Recognition (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (206 citations), Developmental Biology (15 citations), Artificial Intelligence (168 citations), Media Technology (19 citations) and Signal Processing (22 citations). Johan Björck has collaborated with scholars based in United States, China and Finland. Frequent co-authors include Kriti Aggarwal, Owais Khan Mohammed, Furu Wei, Wenhui Wang, Subhojit Som, Saksham Singhal, Dong Li, Hangbo Bao, Qiang Liu and Zhiliang Peng. Their work appears in journals such as Applied Physics Letters, MRS Communications, ACS Combinatorial Science, AI Magazine and International Conference on Machine Learning.
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.