Emre Akbas

Computer Vision and Pattern Recognition · 2010–2026

A researcher in the Science corpus: 5 papers; trusts 922 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 197 citations into it; #8,159 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 115 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 14 citations into it
• Aerospace Engineering — 11 citations into it
• Biomedical Engineering — 11 citations into it; #2,351 of 10,734 by trust here, #6,028 by citations
• Cognitive Neuroscience — 11 citations into it

Whom Emre Akbas trusts overall

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

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

Who trusts Emre Akbas overall

1. Chi Su
2. Rui Zhao
3. Yang Hu
4. Shengcai Liao
5. Alexis Mignon
6. Niki Martinel
7. Martin Hirzer
8. Pritam Sarkar
9. Sateesh Pedagadi
10. Wanli Ouyang

Papers from Emre Akbas's perspective

Ordered by how strongly the researchers Emre Akbas 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. Gradient-based learning applied to document recognition
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Pedestrian Recognition with a Learned Metric (2010) — 353 citations
• Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry (2019) — 298 citations
• Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes. (2017) — 80 citations
• Imbalance Problems in Object Detection: A Review (2019) — 568 citations
• REIGN: Refurbished Embeddings with Integrated Guidance Networks for Efficient Context-Length Scaling (2026)