Jose Dolz

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 399 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #7,568 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 82 citations into it; #11,159 of 35,345 by trust here, #15,866 by citations
• Radiology, Nuclear Medicine and Imaging — 22 citations into it; #543 of 3,265 by trust here, #1,519 by citations
• Model Architectures — 21 citations into it
• Media Technology — 6 citations into it
• Biophysics — 5 citations into it
• Cognitive Neuroscience — 5 citations into it

Whom Jose Dolz trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Evan Shelhamer
7. X. Zhang
8. Ross B. Girshick
9. Shaoqing Ren
10. Ilya Sutskever

Who trusts Jose Dolz overall

1. Xiaohong Huang
2. Luís Pinto-Coelho
3. Yin Dai
4. Satya Prakash Singh
5. Tongxue Zhou
6. Gianni Brauwers
7. José Bernal
8. S. Kevin Zhou
9. Ashish Sinha
10. Andrew Tao

Papers from Jose Dolz's perspective

Ordered by how strongly the researchers Jose Dolz 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need? (2020) — 240 citations
• Multi-Scale Self-Guided Attention for Medical Image Segmentation (2019) — 509 citations
• HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018) — 515 citations
• Boundary loss for highly unbalanced segmentation (2018) — 736 citations
• 3D fully convolutional networks for subcortical segmentation in MRI: A large‐scale study (2016) — 388 citations