Lars Petersson

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 1,316 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 295 citations into it; #5,440 of 21,575 by trust here, #4,584 by citations
• Artificial Intelligence — 205 citations into it; #8,521 of 35,345 by trust here, #7,471 by citations
• Representation & Self-Supervised Learning — 72 citations into it
• Meta & Continual Learning — 42 citations into it
• Radiology, Nuclear Medicine and Imaging — 36 citations into it
• Model Architectures — 33 citations into it
• Computational Mechanics — 30 citations into it
• Evaluation & Benchmarks — 24 citations into it

Whom Lars Petersson trusts overall

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

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

Who trusts Lars Petersson overall

1. Shengkai Wu
2. Ali Cheraghian
3. Xiaoping Li
4. Shafin Rahman
5. Kai Chen
6. Songtao Liu
7. Christian Simon
8. Xiang-Yang Li
9. Zhaojin Huang
10. Dingfu Zhou

Papers from Lars Petersson's perspective

Ordered by how strongly the researchers Lars Petersson 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Improving Object Localization with Fitness NMS and Bounded IoU Loss (2017) — 194 citations
• Encouraging LSTMs to Anticipate Actions Very Early (2017) — 181 citations
• Effective Use of Synthetic Data for Urban Scene Semantic Segmentation (2018) — 164 citations
• Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (2021) — 233 citations
• Bilinear Attention Networks for Person Retrieval (2019) — 156 citations
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (2021) — 81 citations
• Transductive Zero-Shot Learning for 3D Point Cloud Classification (2019) — 68 citations
• Reinforced Attention for Few-Shot Learning and Beyond (2021) — 54 citations
• Mitigating the Hubness Problem for Zero-Shot Learning of 3D Objects (2019) — 48 citations
• Zero-shot Learning of 3D Point Cloud Objects (2019) — 66 citations