Diego Marcos

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 7 papers; trusts 657 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 128 citations into it; #7,212 of 21,575 by trust here, #7,421 by citations
• Artificial Intelligence — 86 citations into it
• Model Architectures — 36 citations into it; #1,177 of 2,278 by trust here, #1,509 by citations
• Media Technology — 24 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 12 citations into it
• Multimodal Learning — 11 citations into it
• Aerospace Engineering — 10 citations into it

Whom Diego Marcos trusts overall

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

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

Who trusts Diego Marcos overall

1. David W. Romero
2. Osman Semih Kayhan
3. Maurice Weiler
4. Erik J. Bekkers
5. Daniel E. Worrall
6. Renata Khasanova
7. Jan van Gemert
8. Simon Graham
9. Fanny Yang
10. Xiuyuan Cheng

Papers from Diego Marcos's perspective

Ordered by how strongly the researchers Diego Marcos 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Rotation Equivariant Vector Field Networks (2016) — 297 citations
• Learning rotation invariant convolutional filters for texture classification (2016) — 153 citations
• Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models (2018) — 261 citations
• Learning Decision Trees Recurrently Through Communication (2021) — 19 citations
• RSVQA: Visual Question Answering for Remote Sensing Data (2020) — 385 citations
• Detecting Mammals in UAV Images: Best Practices to address a substantially Imbalanced Dataset with Deep Learning (2018) — 304 citations
• Scale equivariance in CNNs with vector fields (2018) — 59 citations