Vlad I. Morariu

Computer Vision and Pattern Recognition · 2011–2020

A researcher in the Science corpus: 8 papers; trusts 831 researchers and is trusted by 367 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 186 citations into it; #2,173 of 21,575 by trust here, #2,228 by citations
• Artificial Intelligence — 110 citations into it; #34,066 of 35,345 by trust here, #5,458 by citations
• Model Architectures — 25 citations into it; #2,151 of 2,278 by trust here, #969 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Media Technology — 17 citations into it; #2,453 of 3,621 by trust here, #1,329 by citations
• Multimodal Learning — 14 citations into it
• Aerospace Engineering — 8 citations into it; #2,537 of 6,421 by trust here, #1,628 by citations

Whom Vlad I. Morariu trusts overall

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

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

Who trusts Vlad I. Morariu overall

1. Luisa Verdoliva
2. Yuezun Li
3. Davide Cozzolino
4. Huy Hoang Le Nguyen
5. Yue Wu
6. Yucheng Luo
7. Sheng-Yu Wang
8. Yang He
9. Ning Zhang
10. Yinglin Zheng

Papers from Vlad I. Morariu's perspective

Ordered by how strongly the researchers Vlad I. Morariu 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Birdlets: Subordinate categorization using volumetric primitives and pose-normalized appearance (2011) — 224 citations
• NISP: Pruning Networks Using Neuron Importance Score Propagation (2017) — 883 citations
• Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation (2017) — 326 citations
• Two-Stream Neural Networks for Tampered Face Detection (2017) — 635 citations
• Learning Rich Features for Image Manipulation Detection (2018) — 759 citations
• Learning a Discriminative Filter Bank Within a CNN for Fine-Grained Recognition (2016) — 358 citations
• Modeling Context Between Objects for Referring Expression Understanding (2016) — 569 citations
• Generative-Discriminative Feature Representations for Open-Set Recognition (2020) — 220 citations