Mohammadreza Zolfaghari

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 1,056 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 176 citations into it; #3,878 of 21,575 by trust here, #5,754 by citations
• Artificial Intelligence — 97 citations into it
• Multimodal Learning — 38 citations into it
• Representation & Self-Supervised Learning — 20 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Natural Language Processing — 18 citations into it
• Model Architectures — 16 citations into it
• 3D Vision & Pose — 14 citations into it; #339 of 689 by trust here, #493 by citations

Whom Mohammadreza Zolfaghari trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Karen Simonyan
2. Andrew Zisserman
3. Kaiming He
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Ross B. Girshick
7. Yoshua Bengio
8. Yann LeCun
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Mohammadreza Zolfaghari overall

1. Ji Lin
2. Limin Wang
3. Zuxuan Wu
4. Chenxu Luo
5. Chuang Gan
6. Zehua Sun
7. Jun Liu
8. Boyuan Jiang
9. Jinwoo Choi
10. Alan L. Yuille

Papers from Mohammadreza Zolfaghari's perspective

Ordered by how strongly the researchers Mohammadreza Zolfaghari trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• ECO: Efficient Convolutional Network for Online Video Understanding (2018) — 529 citations
• Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection (2017) — 225 citations
• Orientation-boosted Voxel Nets for 3D Object Recognition (2016) — 233 citations
• COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning (2020) — 181 citations