Haifeng Li

Computer Vision and Pattern Recognition · IEEE transactions on intelligent transportation systems (Print) · 2018–2022

A researcher in the Science corpus: 9 papers; trusts 735 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 237 citations into it
• Artificial Intelligence — 189 citations into it
• Representation & Self-Supervised Learning — 111 citations into it
• Media Technology — 86 citations into it; #3,027 of 3,621 by trust here, #1,079 by citations
• Model Architectures — 42 citations into it
• Evaluation & Benchmarks — 26 citations into it
• Signal Processing — 21 citations into it
• Building and Construction — 19 citations into it

Whom Haifeng Li trusts overall

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

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

Who trusts Haifeng Li overall

1. Valerio Marsocci
2. Wei Zhang
3. Chao Tao

Papers from Haifeng Li's perspective

Ordered by how strongly the researchers Haifeng Li 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. 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

• Remote Sensing Image Scene Classification With Self-Supervised Paradigm Under Limited Labeled Samples (2020) — 112 citations
• FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image (2022) — 25 citations
• Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022) — 91 citations
• Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images (2021) — 153 citations
• DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images (2020) — 616 citations
• A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020) — 370 citations
• CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification (2020) — 66 citations
• Overcoming Long-Term Catastrophic Forgetting Through Adversarial Neural Pruning and Synaptic Consolidation (2019) — 47 citations
• T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction (2018) — 2,914 citations