Triantafyllos Afouras

Computer Vision and Pattern Recognition · 2017–2025

A researcher in the Science corpus: 9 papers; trusts 375 researchers and is trusted by 155 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 166 citations into it; #3,239 of 21,575 by trust here, #6,737 by citations
• Artificial Intelligence — 139 citations into it; #3,720 of 35,345 by trust here, #3,362 by citations
• Signal Processing — 100 citations into it; #925 of 7,572 by trust here, #933 by citations
• Reinforcement Learning — 59 citations into it; #364 of 2,242 by trust here, #347 by citations
• Speech & Audio — 33 citations into it; #338 of 1,076 by trust here, #405 by citations
• Multimodal Learning — 31 citations into it; #306 of 1,211 by trust here, #583 by citations
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 26 citations into it

Whom Triantafyllos Afouras trusts overall

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

1. Joon Son Chung
2. Andrew Zisserman
3. Kaiming He
4. Geoffrey E. Hinton
5. Alex Krizhevsky
6. Yoshua Bengio
7. Ilya Sutskever
8. Alex Graves
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Triantafyllos Afouras overall

1. Ruohan Gao
2. Prajwal K R
3. Joon Son Chung
4. Kristen Grauman
5. Pingchuan Ma
6. Tsubasa Ochiai
7. Rudrabha Mukhopadhyay
8. Andrew Owens
9. Hao Zhu
10. Wei-Ning Hsu

Papers from Triantafyllos Afouras's perspective

Ordered by how strongly the researchers Triantafyllos Afouras 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Counterfactual Multi-Agent Policy Gradients (2017) — 2,656 citations
• The Conversation: Deep Audio-Visual Speech Enhancement (2018) — 397 citations
• Deep Audio-Visual Speech Recognition (2018) — 889 citations
• Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning (2017) — 681 citations
• LRS3-TED: a large-scale dataset for visual speech recognition (2018) — 614 citations
• Self-Supervised Learning of Audio-Visual Objects from Video (2020) — 287 citations
• SAM 3: Segment Anything with Concepts (2025) — 636 citations
• Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives (2023) — 529 citations
• Spot the conversation: speaker diarisation in the wild (2020) — 198 citations