Marios Savvides

Computer Vision and Pattern Recognition · 2004–2022

A researcher in the Science corpus: 14 papers; trusts 1,357 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 375 citations into it; #4,353 of 21,575 by trust here, #2,552 by citations
• Artificial Intelligence — 265 citations into it; #9,406 of 35,345 by trust here, #6,330 by citations
• Representation & Self-Supervised Learning — 79 citations into it
• Efficiency & Compression — 61 citations into it
• Optimization & Training — 49 citations into it; #2,298 of 2,601 by trust here, #1,804 by citations
• Model Architectures — 48 citations into it
• Evaluation & Benchmarks — 38 citations into it
• Generative Models — 27 citations into it

Whom Marios Savvides 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. Christian Szegedy
9. Karen Simonyan
10. Tsung-Yi Lin

Who trusts Marios Savvides overall

1. Zhiqiang Shen
2. Christian Rathgeb
3. Walter J. Scheirer
4. Zhou Lingli
5. Vishal M. Patel
6. Andrew Beng Jin Teoh
7. Nalini K. Ratha
8. Terrance E. Boult
9. Andreas Uhl
10. Zechun Liu

Papers from Marios Savvides's perspective

Ordered by how strongly the researchers Marios Savvides 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. Long Short-Term Memory
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

• Feature Selective Anchor-Free Module for Single-Shot Object Detection (2019) — 917 citations
• Bounding Box Regression With Uncertainty for Accurate Object Detection (2018) — 546 citations
• S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration (2021) — 24 citations
• MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks (2020) — 69 citations
• Soft Anchor-Point Object Detection (2019) — 162 citations
• Solving Missing-Annotation Object Detection with Background Recalibration Loss (2020) — 61 citations
• RankGAN: A Maximum Margin Ranking GAN for Generating Faces (2018) — 24 citations
• Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study (2021) — 83 citations
• FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning (2022) — 477 citations
• ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions (2020) — 434 citations
• Online Ensemble Model Compression Using Knowledge Distillation (2020) — 59 citations
• Un-mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning (2020) — 122 citations
• Image Formation Process (2009) — 1,815 citations
• Cancelable biometric filters for face recognition (2004) — 319 citations