Long Mai

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 10 papers; trusts 1,366 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 345 citations into it; #3,873 of 21,575 by trust here, #4,372 by citations
• Artificial Intelligence — 130 citations into it; #13,970 of 35,345 by trust here, #11,921 by citations
• Generative Models — 53 citations into it
• Evaluation & Benchmarks — 44 citations into it
• Computer Graphics and Computer-Aided Design — 36 citations into it
• Natural Language Processing — 34 citations into it; #3,328 of 5,688 by trust here, #4,601 by citations
• 3D Vision & Pose — 27 citations into it
• Media Technology — 25 citations into it; #1,367 of 3,621 by trust here, #1,079 by citations

Whom Long Mai trusts overall

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

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

Who trusts Long Mai overall

1. Simon Niklaus
2. Wenbo Bao
3. Eden Belouadah
4. Feng Liu
5. Wei-Sheng Lai
6. Zhewei Huang
7. Myungsub Choi
8. Huaizu Jiang
9. Tae-Hyun Oh
10. J. Liew

Papers from Long Mai's perspective

Ordered by how strongly the researchers Long Mai 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. 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

• BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images (2020) — 253 citations
• Video Frame Interpolation via Adaptive Separable Convolution (2017) — 783 citations
• Video Frame Interpolation via Adaptive Convolution (2017) — 554 citations
• Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects (2018) — 329 citations
• Learning to Recover 3D Scene Shape from a Single Image (2020) — 325 citations
• Out of Order: How important is the sequential order of words in a sentence in Natural Language Understanding tasks? (2020) — 129 citations
• Interactive Boundary Prediction for Object Selection (2018) — 38 citations
• M2KD: Multi-model and Multi-level Knowledge Distillation for Incremental Learning (2019) — 58 citations
• Deep Interactive Thin Object Selection (2021) — 65 citations
• MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input (2019) — 40 citations