J. Lai

Computer Vision and Pattern Recognition · IEEE International Conference on Computer Vision · 2010–2020

A researcher in the Science corpus: 21 papers; trusts 1,548 researchers and is trusted by 47 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 574 citations into it; #4,081 of 21,575 by trust here, #2,570 by citations
• Artificial Intelligence — 371 citations into it; #6,178 of 35,345 by trust here, #4,420 by citations
• Natural Language Processing — 125 citations into it; #3,894 of 5,688 by trust here, #2,660 by citations
• Biomedical Engineering — 95 citations into it; #4,157 of 10,734 by trust here, #4,781 by citations
• Model Architectures — 90 citations into it
• Human-Computer Interaction — 47 citations into it
• Representation & Self-Supervised Learning — 44 citations into it; #973 of 3,145 by trust here, #1,415 by citations
• Optimization & Training — 42 citations into it

Whom J. Lai trusts overall

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

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

Who trusts J. Lai overall

1. Jun Liu
2. Amir Shahroudy
3. Jianfang Hu
4. Pengfei Zhang
5. Xiatian Zhu
6. Mang Ye
7. Qiuhong Ke
8. Pichao Wang
9. Lijun Wu
10. G. Wang

Papers from J. Lai's perspective

Ordered by how strongly the researchers J. Lai 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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

• Partial Person Re-Identification (2015) — 301 citations
• Progressive Teacher-Student Learning for Early Action Prediction (2019) — 137 citations
• Jointly Learning Heterogeneous Features for RGB-D Activity Recognition (2015) — 557 citations
• Interleaved Structured Sparse Convolutional Neural Networks (2018) — 110 citations
• RGB-Infrared Cross-Modality Person Re-identification (2017) — 796 citations
• Adversarial Neural Machine Translation (2017) — 137 citations
• Security algorithm of face recognition based on local binary pattern and random projection (2010) — 6 citations
• A Study of Reinforcement Learning for Neural Machine Translation (2018) — 189 citations
• Distilled Person Re-Identification: Towards a More Scalable System (2019) — 97 citations
• Depth Growing for Neural Machine Translation (2019) — 45 citations
• Learning to Teach with Dynamic Loss Functions (2018) — 126 citations
• Deep Bilinear Learning for RGB-D Action Recognition (2018) — 84 citations
• Person Re-Identification by Camera Correlation Aware Feature Augmentation (2017) — 334 citations
• Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection (2020) — 366 citations
• Exploiting Monolingual Data at Scale for Neural Machine Translation (2019) — 72 citations
• Early Action Prediction by Soft Regression (2019) — 117 citations
• Ultra-Scalable Spectral Clustering and Ensemble Clustering (2019) — 448 citations
• Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (2018) — 56 citations
• Deep Growing Learning (2017) — 34 citations
• Real-Time RGB-D Activity Prediction by Soft Regression (2016) — 93 citations