Ahmet Iscen

Computer Vision and Pattern Recognition · 2018–2025

A researcher in the Science corpus: 6 papers; trusts 804 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 186 citations into it; #13,262 of 21,575 by trust here, #7,584 by citations
• Artificial Intelligence — 132 citations into it; #22,767 of 35,345 by trust here, #9,641 by citations
• Representation & Self-Supervised Learning — 70 citations into it
• Information Retrieval & Search — 25 citations into it
• Aerospace Engineering — 21 citations into it
• Meta & Continual Learning — 18 citations into it; #877 of 1,041 by trust here, #547 by citations
• Model Architectures — 16 citations into it
• Optimization & Training — 14 citations into it

Whom Ahmet Iscen trusts overall

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

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

Who trusts Ahmet Iscen overall

1. Liyuan Wang
2. Da-Wei Zhou
3. Enrico Fini
4. Arthur Douillard
5. Chong Zhou
6. Chongxuan Li
7. Yaoyao Liu
8. Jian Liang
9. Youngmin Oh
10. Huyong Wang

Papers from Ahmet Iscen's perspective

Ordered by how strongly the researchers Ahmet Iscen 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking (2018) — 459 citations
• Memory-Efficient Incremental Learning Through Feature Adaptation (2020) — 207 citations
• Label Propagation for Deep Semi-Supervised Learning (2019) — 731 citations
• Reveal: Retrieval-Augmented Visual-Language Pre-Training with Multi-Source Multimodal Knowledge Memory (2022) — 178 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Mining on Manifolds: Metric Learning Without Labels (2018) — 122 citations