Michael Lam
Impact in
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- Video Analysis and Summarization
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Image Retrieval and Classification Techniques
- Human Pose and Action Recognition
- Signal Processing top 5%
- Music and Audio Processing
Papers in
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- Advanced Image and Video Retrieval Techniques 2
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- Domain Adaptation and Few-Shot Learning 1
- Advanced Text Analysis Techniques 1
- Co-authors
- Siniša Todorović (4 shared papers)Behrooz Mahasseni (2 shared papers)Daniela Raicu (1 shared paper)Jacob Furst (1 shared paper)Janardhan Rao Doppa (2 shared papers)Thomas G. Dietterich (2 shared papers)Michael Gibbert (1 shared paper)Zachary Estes (1 shared paper)
- Journals
- Journal of Cognitive Psychology (1 paper)Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE (1 paper)
- Partner nations
- United StatesItalySwitzerland
In The Last Decade
Michael Lam
6 papers receiving 563 citations
Michael Lam's Hit Papers
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 523
- Signal Processing 261
- Artificial Intelligence 114
- Developmental Biology 6
- Sociology and Political Science 79
Countries citing papers authored by Michael Lam
This map shows the geographic impact of Michael Lam'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 Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Lam more than expected).
Fields of papers citing papers by Michael Lam
This network shows the impact of papers produced by Michael Lam. 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 Lam. The network helps show where Michael Lam may publish in the future.
Co-authors
The 12 scholars most cited alongside Michael Lam, 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 | Unsupervised Video Summarization with Adversarial LSTM Networks Hit paper breakdown → | 2017 | 422 |
| 2 | 2017 | 97 | |
| 3 | 2007 | 35 | |
| 4 | 2015 | 13 | |
| 5 | 2016 | 10 | |
| 6 | 2013 | 7 |
About Michael Lam
Michael Lam is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Nature and Landscape Conservation, Paleontology and Social Psychology, having authored 6 papers that have together received 584 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Color perception and design (1 paper), Fish biology, ecology, and behavior (1 paper), Video Coding and Compression Technologies (1 paper), Marine Invertebrate Physiology and Ecology (1 paper), Advanced Text Analysis Techniques (1 paper) and Music and Audio Processing (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (523 citations), Signal Processing (261 citations), Artificial Intelligence (114 citations), Developmental Biology (6 citations) and Sociology and Political Science (79 citations). Michael Lam has collaborated with scholars based in United States, Italy and Switzerland. Frequent co-authors include Siniša Todorović, Behrooz Mahasseni, Daniela Raicu, Jacob Furst, Janardhan Rao Doppa, Thomas G. Dietterich, Michael Gibbert, Zachary Estes, David Mazursky and Hu Xu. Their work appears in journals such as Journal of Cognitive Psychology and Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE.
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.