Christos Sakaridis

Computer Vision and Pattern Recognition · 2017–2024

A researcher in the Science corpus: 6 papers; trusts 1,152 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 251 citations into it; #3,705 of 21,575 by trust here, #4,286 by citations
• Artificial Intelligence — 106 citations into it; #21,017 of 35,345 by trust here, #10,942 by citations
• Evaluation & Benchmarks — 37 citations into it
• Media Technology — 33 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Aerospace Engineering — 26 citations into it
• Automotive Engineering — 15 citations into it
• Model Architectures — 13 citations into it

Whom Christos Sakaridis 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. Diederik P. Kingma
7. Yann LeCun
8. Shaoqing Ren
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Christos Sakaridis overall

1. Dengxin Dai
2. Lukas Hoyer
3. Luc Van Gool
4. Zhenwei He
5. Feifan Lv
6. Kuniaki Saito
7. Jinhong Deng
8. Taekyung Kim
9. Xinge Zhu
10. Nataniel Ruiz

Papers from Christos Sakaridis's perspective

Ordered by how strongly the researchers Christos Sakaridis 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Domain Adaptive Faster R-CNN for Object Detection in the Wild (2018) — 1,565 citations
• Semantic Foggy Scene Understanding with Synthetic Data (2017) — 1,414 citations
• Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding (2018) — 289 citations
• Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation (2019) — 306 citations
• UniDepth: Universal Monocular Metric Depth Estimation (2024) — 455 citations
• ACDC: The Adverse Conditions Dataset with Correspondences for Semantic Driving Scene Understanding (2021) — 701 citations