Humam Alwassel

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 721 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 129 citations into it; #9,309 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 74 citations into it; #35,227 of 35,345 by trust here, #28,481 by citations
• Representation & Self-Supervised Learning — 36 citations into it; #3,138 of 3,145 by trust here, #1,781 by citations
• Evaluation & Benchmarks — 20 citations into it; #3,134 of 3,147 by trust here, #2,903 by citations
• Signal Processing — 15 citations into it; #7,564 of 7,572 by trust here, #2,184 by citations
• Model Architectures — 11 citations into it
• Multimodal Learning — 10 citations into it
• Endocrinology, Diabetes and Metabolism — 8 citations into it

Whom Humam Alwassel trusts overall

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

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

Who trusts Humam Alwassel overall

1. Christoph Feichtenhofer
2. Chen Zhao
3. Bruno Korbar
4. Daochang Liu
5. Guillaume Vaudaux-Ruth
6. Haytham M. Fayek
7. Chen-Lin Zhang
8. Yonglong Tian
9. Pedro Morgado
10. Jiangliu Wang

Papers from Humam Alwassel's perspective

Ordered by how strongly the researchers Humam Alwassel 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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

• Self-Supervised Learning by Cross-Modal Audio-Video Clustering (2019) — 478 citations
• Diagnosing Error in Temporal Action Detectors (2018) — 123 citations
• Action Search: Spotting Actions in Videos and Its Application to Temporal Action Localization (2017) — 85 citations