Matt Feiszli
Computer Vision and Pattern Recognition · 2019–2025
A researcher in the Science corpus: 7 papers; trusts 1,157 researchers and is trusted by 196 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 302 citations into it; #2,692 of 21,575 by trust here, #4,402 by citations
- Artificial Intelligence — 155 citations into it; #11,756 of 35,345 by trust here, #6,517 by citations
- Evaluation & Benchmarks — 55 citations into it
- Model Architectures — 32 citations into it
- Representation & Self-Supervised Learning — 24 citations into it
- Optimization & Training — 22 citations into it
- Aerospace Engineering — 18 citations into it
- Multimodal Learning — 17 citations into it; #941 of 1,211 by trust here, #920 by citations
Whom Matt Feiszli trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Ross B. Girshick
- Alex Krizhevsky
- Geoffrey E. Hinton
- Andrew Zisserman
- Karen Simonyan
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Diederik P. Kingma
Who trusts Matt Feiszli overall
- Weiyao Wang
- Bruno Korbar
- Mandela Patrick
- Du Tran
- Rohit Girdhar
- Christoph Feichtenhofer
- Haodong Duan
- Zehua Sun
- Muhammet Esat Kalfaoglu
- Po-Yao (Bernie) Huang
Papers from Matt Feiszli's perspective
Ordered by how strongly the researchers Matt Feiszli trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Large-Scale Weakly-Supervised Pre-Training for Video Action Recognition (2019) — 319 citations
- Video Classification With Channel-Separated Convolutional Networks (2019) — 655 citations
- What Makes Training Multi-Modal Classification Networks Hard? (2019) — 665 citations
- Unidentified Video Objects: A Benchmark for Dense, Open-World Segmentation (2021) — 157 citations
- Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass (2025) — 356 citations
- Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity (2022) — 56 citations
- Only Time Can Tell: Discovering Temporal Data for Temporal Modeling (2019) — 98 citations