Clayton Mellina

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 2 papers; trusts 350 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 62 citations into it; #8,275 of 21,575 by trust here, #14,478 by citations
• Artificial Intelligence — 42 citations into it; #12,747 of 35,345 by trust here, #20,377 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Optimization & Training — 16 citations into it
• Aerospace Engineering — 8 citations into it
• Information Retrieval & Search — 8 citations into it
• Model Architectures — 6 citations into it

Whom Clayton Mellina trusts overall

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

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

Who trusts Clayton Mellina overall

1. Filip Radenovic
2. Giorgos Tolias
3. Edo Collins
4. Liang Zheng
5. Hyo Jin Kim
6. Xiu-Shen Wei
7. Ondřej Chum
8. Qi Wang
9. Marina E. Plissiti

Papers from Clayton Mellina's perspective

Ordered by how strongly the researchers Clayton Mellina 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Cross-Dimensional Weighting for Aggregated Deep Convolutional Features (2015) — 455 citations
• CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning (2021) — 337 citations