Michael Gygli
- Computer Vision and Pattern Recognition top 2%
- Signal Processing top 2%
- Artificial Intelligence top 10%
- Sociology and Political Science
- Cognitive Neuroscience
- Co-authors
- Luc Van GoolHelmut GräbnerNaoya TakahashiLiangliang CaoYale SongHayko RiemenschneiderBeat PfisterFabian Nater
- Topics
- Advanced Image and Video Retrieval Techniques (7 papers)Video Analysis and Summarization (6 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- IEEE Transactions on MultimediaLirias (KU Leuven)MediaEval
- Partner nations
- SwitzerlandBelgiumUnited States
In The Last Decade
Michael Gygli
14 papers receiving 726 citations
Peers
Comparison fields: 5 of 78
- Computer Vision and Pattern Recognition 601
- Signal Processing 317
- Artificial Intelligence 133
- Sociology and Political Science 76
- Cognitive Neuroscience 42
Countries citing papers authored by Michael Gygli
This map shows the geographic impact of Michael Gygli'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 Michael Gygli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Gygli more than expected).
Fields of papers citing papers by Michael Gygli
This network shows the impact of papers produced by Michael Gygli. 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 Michael Gygli. The network helps show where Michael Gygli may publish in the future.
Co-authorship network of co-authors of Michael Gygli
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Gygli. A scholar is included among the top collaborators of Michael Gygli 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 Michael Gygli. Michael Gygli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 114 | |
| 3 | 34 | |
| 4 | 4 | |
| 5 | 99 | |
| 6 | ETH-CVL @ MediaEval 2016: Textual-Visual Embeddings and Video2GIF for Video Interestingness. | 3 |
| 7 | 87 | |
| 8 | 14 | |
| 9 | 10 | |
| 10 | 263 | |
| 11 | ETH-CVL @ MediaEval 2015: Learning Objective Functions for Improved Image Retrieval | 1 |
| 12 | 7 | |
| 13 | 113 | |
| 14 | 11 |
About Michael Gygli
Michael Gygli is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Human-Computer Interaction, having authored 14 papers that have together received 761 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (7 papers), Video Analysis and Summarization (6 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Signal Processing (317 citations), Computer Vision and Pattern Recognition (601 citations) and Developmental Biology (21 citations). Michael Gygli has collaborated with scholars based in Switzerland, Belgium and United States. Frequent co-authors include Luc Van Gool, Helmut Gräbner, Naoya Takahashi, Liangliang Cao, Yale Song, Hayko Riemenschneider, Beat Pfister, Fabian Nater, Dengxin Dai and Santiago Manén. Their work appears in journals such as IEEE Transactions on Multimedia, Lirias (KU Leuven) and MediaEval.
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.