Rynson W. H. Lau

Computer Vision and Pattern Recognition · 2013–2023

A researcher in the Science corpus: 13 papers; trusts 1,617 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 442 citations into it; #6,299 of 21,575 by trust here, #3,361 by citations
• Artificial Intelligence — 143 citations into it
• Media Technology — 62 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Model Architectures — 45 citations into it
• Generative Models — 23 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Optimization & Training — 22 citations into it

Whom Rynson W. H. Lau trusts overall

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

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

Who trusts Rynson W. H. Lau overall

1. Martin Danelljan
2. Fahad Shahbaz Khan
3. Goutam Bhat
4. Michael Felsberg
5. Gustav Häger
6. Zhong-Qiu Zhao
7. Wenguan Wang
8. Runmin Cong
9. Lihe Yang

Papers from Rynson W. H. Lau's perspective

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

Their papers

• Distilling Localization for Self-Supervised Representation Learning (2020) — 59 citations
• What makes instance discrimination good for transfer learning? (2020) — 183 citations
• CREST: Convolutional Residual Learning for Visual Tracking (2017) — 503 citations
• VITAL: VIsual Tracking via Adversarial Learning (2018) — 533 citations
• Guided Collaborative Training for Pixel-wise Semi-Supervised Learning (2020) — 296 citations
• Visual Tracking via Locality Sensitive Histograms (2013) — 328 citations
• Dynamic Scene Deblurring Using Spatially Variant Recurrent Neural Networks (2018) — 381 citations
• Oriented Object Proposals (2015) — 31 citations
• SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection (2015) — 297 citations
• BiFormer: Vision Transformer with Bi-Level Routing Attention (2023) — 1,025 citations
• Learning to Restore Low-Light Images via Decomposition-and-Enhancement (2020) — 443 citations
• Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset (2019) — 677 citations
• DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal (2017) — 347 citations