Ali Diba

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 1,366 researchers and is trusted by 128 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 284 citations into it; #2,990 of 21,575 by trust here, #4,926 by citations
• Artificial Intelligence — 139 citations into it; #8,270 of 35,345 by trust here, #6,956 by citations
• Evaluation & Benchmarks — 35 citations into it
• Model Architectures — 29 citations into it
• Media Technology — 16 citations into it
• Representation & Self-Supervised Learning — 16 citations into it; #3,110 of 3,145 by trust here, #1,982 by citations
• Biomedical Engineering — 14 citations into it; #2,893 of 10,734 by trust here, #6,932 by citations
• Endocrinology, Diabetes and Metabolism — 14 citations into it

Whom Ali Diba trusts overall

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

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

Who trusts Ali Diba overall

1. Vivek Sharma
2. Yi Zhu
3. Rohit Girdhar
4. Luc Van Gool
5. Christoph Feichtenhofer
6. Fang Wan
7. Limin Wang
8. Dongliang He
9. Juergen Gall
10. Rainer Stiefelhagen

Papers from Ali Diba's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Weakly Supervised Cascaded Convolutional Networks (2016) — 312 citations
• Deep Temporal Linear Encoding Networks (2016) — 240 citations
• DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers (2015) — 122 citations
• DynamoNet: Dynamic Action and Motion Network (2019) — 124 citations
• Spatio-Temporal Channel Correlation Networks for Action Classification (2018) — 197 citations
• Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification (2016) — 77 citations
• Classification-Driven Dynamic Image Enhancement (2017) — 71 citations
• 3D CNNs with Adaptive Temporal Feature Resolutions (2020) — 45 citations
• Holistic Large Scale Video Understanding (2019) — 37 citations