Matt Feiszli
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Biomedical Engineering
- Signal Processing top 10%
- Human-Computer Interaction top 5%
- Co-authors
- Du TranWei‐Yao WangHeng WangLorenzo TorresaniStan Weixian LeiDeepti GhadiyaramMike Zheng ShouYi Yang
- Topics
- Human Pose and Action Recognition (9 papers)Multimodal Machine Learning Applications (8 papers)Anomaly Detection Techniques and Applications (7 papers)
- Journals
- Applied and Computational Harmonic Analysis2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Partner nations
- IsraelUnited StatesSingapore
In The Last Decade
Matt Feiszli
19 papers receiving 914 citations
Hit Papers
Peers
Comparison fields: 5 of 100
- Computer Vision and Pattern Recognition 698
- Artificial Intelligence 465
- Biomedical Engineering 91
- Signal Processing 85
- Human-Computer Interaction 58
Countries citing papers authored by Matt Feiszli
This map shows the geographic impact of Matt Feiszli'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 Matt Feiszli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matt Feiszli more than expected).
Fields of papers citing papers by Matt Feiszli
This network shows the impact of papers produced by Matt Feiszli. 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 Matt Feiszli. The network helps show where Matt Feiszli may publish in the future.
Co-authorship network of co-authors of Matt Feiszli
This figure shows the co-authorship network connecting the top 25 collaborators of Matt Feiszli. A scholar is included among the top collaborators of Matt Feiszli 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 Matt Feiszli. Matt Feiszli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 38 | |
| 5 | 1 | |
| 6 | 22 | |
| 7 | 13 | |
| 8 | 7 | |
| 9 | 28 | |
| 10 | 34 | |
| 11 | 61 | |
| 12 | 33 | |
| 13 | What Makes Training Multi-Modal Classification Networks Hard?breakdown → | 296 |
| 14 | What Makes Training Multi-Modal Networks Hard? | 10 |
| 15 | FASTER Recurrent Networks for Video Classification. | 1 |
| 16 | Video Classification With Channel-Separated Convolutional Networksbreakdown → | 375 |
| 17 | 3 | |
| 18 | 8 | |
| 19 | Conformal shape representation | 2 |
| 20 | 4 |
About Matt Feiszli
Matt Feiszli is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics, having authored 20 papers that have together received 942 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (9 papers), Multimodal Machine Learning Applications (8 papers) and Anomaly Detection Techniques and Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (698 citations), Artificial Intelligence (465 citations) and Human-Computer Interaction (58 citations). Matt Feiszli has collaborated with scholars based in Israel, United States and Singapore. Frequent co-authors include Du Tran, Wei‐Yao Wang, Heng Wang, Lorenzo Torresani, Heng Wang, Stan Weixian Lei, Deepti Ghadiyaram, Mike Zheng Shou, Yi Yang and Heng Wang. Their work appears in journals such as Applied and Computational Harmonic Analysis, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.