Matt Feiszli

2.1k total citations · 2 hit papers
20 papers, 942 citations indexed

About

Matt Feiszli is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Matt Feiszli has authored 20 papers receiving a total of 942 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 10 papers in Artificial Intelligence and 2 papers in Computational Mechanics. Recurrent topics in Matt Feiszli's work include Human Pose and Action Recognition (9 papers), Multimodal Machine Learning Applications (8 papers) and Anomaly Detection Techniques and Applications (7 papers). Matt Feiszli is often cited by papers focused on Human Pose and Action Recognition (9 papers), Multimodal Machine Learning Applications (8 papers) and Anomaly Detection Techniques and Applications (7 papers). Matt Feiszli collaborates with scholars based in Israel, United States and Singapore. Matt Feiszli's co-authors include Du Tran, Wei‐Yao Wang, Heng Wang, Lorenzo Torresani, Heng Wang, Mike Zheng Shou, Stan Weixian Lei, Deepti Ghadiyaram, Yi Yang and Jitendra Malik and has published in prestigious 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).

In The Last Decade

Matt Feiszli

19 papers receiving 914 citations

Hit Papers

Video Classification With... 2019 2026 2021 2023 2019 2020 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Video Classification With Channel-Separated Convolutional Networks
    2019 375 citations Du Tran, Heng Wang et al. profile →
  2. What Makes Training Multi-Modal Classification Networks Hard?
    2020 296 citations Wei‐Yao Wang, Du Tran et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Matt Feiszli Israel 10 698 465 91 85 58 20 942
Rohit Girdhar United States 13 794 1.1× 388 0.8× 70 0.8× 79 0.9× 46 0.8× 19 1.2k
Avinash Ravichandran United States 11 831 1.2× 465 1.0× 118 1.3× 107 1.3× 62 1.1× 18 1.1k
Mike Zheng Shou Singapore 18 1.1k 1.6× 451 1.0× 80 0.9× 119 1.4× 44 0.8× 74 1.4k
Hedi Tabia France 13 649 0.9× 164 0.4× 113 1.2× 44 0.5× 106 1.8× 42 788
Luoqi Liu China 20 1.3k 1.9× 348 0.7× 61 0.7× 110 1.3× 31 0.5× 38 1.5k
Pietro Morerio Italy 17 552 0.8× 280 0.6× 91 1.0× 66 0.8× 123 2.1× 62 849
Shoou-I Yu United States 17 691 1.0× 316 0.7× 37 0.4× 75 0.9× 24 0.4× 31 891
Vojtěch Franc Czechia 15 378 0.5× 333 0.7× 204 2.2× 156 1.8× 33 0.6× 31 927

Countries citing papers authored by Matt Feiszli

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Sax, Alexander F., Kevin J Liang, Mikael Henaff, et al.. (2025). Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass. 21924–21935. 2 indexed citations
2.
3.
Yang, Xitong, et al.. (2023). Relational Space-Time Query in Long-Form Videos. 6398–6408. 4 indexed citations
4.
Wang, Wei‐Yao, et al.. (2023). SiLK: Simple Learned Keypoints. 22442–22451. 38 indexed citations
5.
Huang, Jing, Kevin J Liang, Xingyu Chen, et al.. (2023). Self-Supervised Object Detection from Egocentric Videos. 5202–5214. 1 indexed citations
6.
Feiszli, Matt, et al.. (2022). Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4412–4422. 22 indexed citations
7.
Gong, Xinyu, Heng Wang, Zheng Shou, et al.. (2021). Searching for Two-Stream Models in Multivariate Space for Video Recognition. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8013–8022. 7 indexed citations
8.
Fan, Haoqi, Tullie Murrell, Heng Wang, et al.. (2021). PyTorchVideo. 3783–3786. 28 indexed citations
9.
Yan, Zhicheng, Xiaoliang Dai, Peizhao Zhang, et al.. (2021). FP-NAS: Fast Probabilistic Neural Architecture Search. 15134–15143. 13 indexed citations
10.
Shou, Mike Zheng, Stan Weixian Lei, Wei‐Yao Wang, Deepti Ghadiyaram, & Matt Feiszli. (2021). Generic Event Boundary Detection: A Benchmark for Event Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 8055–8064. 34 indexed citations
11.
Wang, Wei‐Yao, Matt Feiszli, Heng Wang, & Du Tran. (2021). Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 10756–10765. 61 indexed citations
12.
Zhu, Linchao, Du Tran, Laura Sevilla-Lara, et al.. (2020). FASTER Recurrent Networks for Efficient Video Classification. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 13098–13105. 33 indexed citations
13.
Wang, Wei‐Yao, Du Tran, & Matt Feiszli. (2020). What Makes Training Multi-Modal Classification Networks Hard?. 12692–12702. 296 indexed citations breakdown →
14.
Wang, Wei‐Yao, Du Tran, & Matt Feiszli. (2019). What Makes Training Multi-Modal Networks Hard?. arXiv (Cornell University). 10 indexed citations
15.
Zhu, Linchao, Laura Sevilla-Lara, Du Tran, et al.. (2019). FASTER Recurrent Networks for Video Classification.. arXiv (Cornell University). 1 indexed citations
16.
Tran, Du, Heng Wang, Matt Feiszli, & Lorenzo Torresani. (2019). Video Classification With Channel-Separated Convolutional Networks. 5551–5560. 375 indexed citations breakdown →
17.
Feiszli, Matt, et al.. (2014). Metric spaces of shapes and applications: compression, curve matching and low-dimensional representation. 1(2). 173–221. 3 indexed citations
18.
Feiszli, Matt & Peter W. Jones. (2011). Curve denoising by multiscale singularity detection and geometric shrinkage. Applied and Computational Harmonic Analysis. 31(3). 392–409. 8 indexed citations
19.
Mumford, David & Matt Feiszli. (2008). Conformal shape representation. 2 indexed citations
20.
Feiszli, Matt & David Mumford. (2007). Shape representation via conformal mapping. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6498. 64980G–64980G. 4 indexed citations

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.

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