Matthew R. Scott
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 2%
- Cognitive Neuroscience top 10%
- Psychiatry and Mental health top 10%
- Radiology, Nuclear Medicine and Imaging top 10%
- Topics
- Alzheimer's disease research and treatments (10 papers)Dementia and Cognitive Impairment Research (8 papers)Functional Brain Connectivity Studies (8 papers)
- Partner nations
- United StatesChinaAustralia
In The Last Decade
Matthew R. Scott
45 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 138
- Computer Vision and Pattern Recognition 1.5k
- Artificial Intelligence 576
- Cognitive Neuroscience 218
- Psychiatry and Mental health 176
- Radiology, Nuclear Medicine and Imaging 172
Countries citing papers authored by Matthew R. Scott
This map shows the geographic impact of Matthew R. Scott'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 Matthew R. Scott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew R. Scott more than expected).
Fields of papers citing papers by Matthew R. Scott
This network shows the impact of papers produced by Matthew R. Scott. 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 Matthew R. Scott. The network helps show where Matthew R. Scott may publish in the future.
Co-authorship network of co-authors of Matthew R. Scott
This figure shows the co-authorship network connecting the top 25 collaborators of Matthew R. Scott. A scholar is included among the top collaborators of Matthew R. Scott 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 Matthew R. Scott. Matthew R. Scott 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 | 0 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 34 | |
| 6 | 79 | |
| 7 | 4 | |
| 8 | Deformable Siamese Attention Networks for Visual Object Trackingbreakdown → | 312 |
| 9 | V4D: 4D Covolutional Neural Networks for Video-level Representations Learning | 5 |
| 10 | 34 | |
| 11 | 36 | |
| 12 | 29 | |
| 13 | 168 | |
| 14 | 155 | |
| 15 | 40 | |
| 16 | Multi-Similarity Loss With General Pair Weighting for Deep Metric Learningbreakdown → | 493 |
| 17 | 39 | |
| 18 | 59 | |
| 19 | Engkoo: Mining the Web for Language Learning | 1 |
| 20 | A case study of strategic infarct dementia investigated with the cognitive assessment system. | 3 |
About Matthew R. Scott
Matthew R. Scott is a scholar working on Computer Vision and Pattern Recognition, Psychiatry and Mental health and Human-Computer Interaction, having authored 47 papers that have together received 2.2k indexed citations. Recurring topics across this work include Alzheimer's disease research and treatments (10 papers), Dementia and Cognitive Impairment Research (8 papers) and Functional Brain Connectivity Studies (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Human-Computer Interaction (114 citations) and Artificial Intelligence (576 citations). Matthew R. Scott has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Weilin Huang, Xun Wang, Xintong Han, Yilei Xiong, Xiaojun Hu, Xintong Han, Haozhi Zhang, Zhi Tian, Linjie Xing and Yu Gao. Their work appears in journals such as Circulation, Nature Communications and NeuroImage.
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.