Yancheng Bai

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 8 papers; trusts 1,506 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 288 citations into it; #13,774 of 21,575 by trust here, #9,187 by citations
• Artificial Intelligence — 95 citations into it
• Media Technology — 33 citations into it; #3,577 of 3,621 by trust here, #1,498 by citations
• Generative Models — 28 citations into it; #1,943 of 1,956 by trust here, #1,382 by citations
• Model Architectures — 28 citations into it
• Optimization & Training — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Industrial and Manufacturing Engineering — 11 citations into it

Whom Yancheng Bai trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Karen Simonyan
7. Shaoqing Ren
8. Christian Szegedy
9. Ilya Sutskever
10. Sergey Ioffe

Who trusts Yancheng Bai overall

1. Yongqiang Zhang
2. Ming Ding
3. Gong Cheng
4. Bernard Ghanem
5. Poojan Oza
6. Zhiqiang Shen
7. Abdul Jabbar
8. Guang Chen
9. Zhengxia Zou
10. Jakaria Rabbi

Papers from Yancheng Bai's perspective

Ordered by how strongly the researchers Yancheng Bai 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. 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

• SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network (2018) — 429 citations
• W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection (2018) — 130 citations
• Multi-task Generative Adversarial Network for Detecting Small Objects in the Wild (2020) — 46 citations
• KGSNet: Key-Point-Guided Super-Resolution Network for Pedestrian Detection in the Wild (2020) — 28 citations
• Detecting small faces in the wild based on generative adversarial network and contextual information (2019) — 33 citations
• Learning a strong detector for action localization in videos (2019) — 10 citations
• Finding Tiny Faces in the Wild with Generative Adversarial Network (2018) — 210 citations
• Multi-scale Fully Convolutional Network for Face Detection in the Wild (2017) — 21 citations