Marc Proesmans

Computer Vision and Pattern Recognition · 1996–2020

A researcher in the Science corpus: 6 papers; trusts 745 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #7,327 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 120 citations into it
• Representation & Self-Supervised Learning — 58 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 15 citations into it
• Media Technology — 12 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Marc Proesmans trusts overall

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

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

Who trusts Marc Proesmans overall

1. Li Zhang
2. Huiyu Wang
3. Xiuming Zhang
4. Brian Curless
5. Liang-Chieh Chen
6. Ujwal D. Bonde
7. Yukun Zhu
8. Qihang Yu
9. S. M. Seitz
10. Zhoutong Zhang

Papers from Marc Proesmans's perspective

Ordered by how strongly the researchers Marc Proesmans 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. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth (2019) — 256 citations
• Learning To Classify Images Without Labels (2020) — 677 citations
• Fast Scene Understanding for Autonomous Driving (2017) — 77 citations
• Towards End-to-End Lane Detection: an Instance Segmentation Approach (2018) — 804 citations
• Multi-Task Learning for Dense Prediction Tasks: A Survey (2020) — 994 citations
• One-shot active 3D shape acquisition (1996)