Yiquan Wu

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 517 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it
• Artificial Intelligence — 74 citations into it
• Model Architectures — 43 citations into it
• Aerospace Engineering — 30 citations into it; #604 of 6,421 by trust here, #2,921 by citations
• Electrical and Electronic Engineering — 15 citations into it; #2,127 of 16,539 by trust here, #13,165 by citations
• Media Technology — 14 citations into it; #2,597 of 3,621 by trust here, #2,820 by citations
• Optimization & Training — 10 citations into it
• Computational Mechanics — 9 citations into it

Whom Yiquan Wu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Ross B. Girshick
4. Geoffrey E. Hinton
5. Shaoqing Ren
6. Yoshua Bengio
7. Christian Szegedy
8. X. Zhang
9. Yann LeCun
10. Tsung-Yi Lin

Who trusts Yiquan Wu overall

1. Xin Wu
2. Landan Zhang
3. Yimian Dai
4. Boyang Li
5. Tianfang Zhang
6. Mingjin Zhang
7. Danfeng Hong
8. Zhenming Peng
9. Yang Sun
10. Huan Wang

Papers from Yiquan Wu's perspective

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

Their papers

• Reweighted Infrared Patch-Tensor Model With Both Nonlocal and Local Priors for Single-Frame Small Target Detection (2017) — 547 citations
• Recent advances in small object detection based on deep learning: A review (2020) — 546 citations
• Attentional Feature Fusion (2020) — 979 citations
• Asymmetric Contextual Modulation for Infrared Small Target Detection (2020) — 723 citations
• Attentional Local Contrast Networks for Infrared Small Target Detection (2020) — 744 citations