Matthew R. Scott

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 958 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 227 citations into it; #9,397 of 21,575 by trust here, #5,287 by citations
• Artificial Intelligence — 99 citations into it; #16,013 of 35,345 by trust here, #8,365 by citations
• Model Architectures — 33 citations into it
• Evaluation & Benchmarks — 27 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 18 citations into it
• Media Technology — 10 citations into it

Whom Matthew R. Scott trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Ross B. Girshick
4. Alex Krizhevsky
5. Diederik P. Kingma
6. Karen Simonyan
7. Jia Deng
8. Christian Szegedy
9. Yoshua Bengio
10. Andrew Zisserman

Who trusts Matthew R. Scott overall

1. Juan R. Terven
2. Timo Milbich

Papers from Matthew R. Scott's perspective

Ordered by how strongly the researchers Matthew R. Scott 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. Attention is All you Need (Neural Information Processing Systems)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images (2018) — 383 citations
• Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning (2019) — 909 citations
• Deep Metric Learning with Hierarchical Triplet Loss (2018) — 465 citations
• Deformable Siamese Attention Networks for Visual Object Tracking (2020) — 369 citations
• Decoupling Category-wise Independence and Relevance with Self-attention for Multi-label Image Classification (2019) — 13 citations
• TOOD: Task-aligned One-stage Object Detection (2021) — 1,333 citations
• Knowledge Integration Networks for Action Recognition (2020) — 22 citations
• ClothFlow: A Flow-Based Model for Clothed Person Generation (2019) — 307 citations