Yongqiang Zhang

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 11 papers; trusts 1,338 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 329 citations into it; #11,288 of 21,575 by trust here, #7,657 by citations
• Artificial Intelligence — 143 citations into it
• Media Technology — 32 citations into it; #3,579 of 3,621 by trust here, #1,498 by citations
• Model Architectures — 32 citations into it
• Generative Models — 29 citations into it; #1,944 of 1,956 by trust here, #1,382 by citations
• Evaluation & Benchmarks — 26 citations into it
• Optimization & Training — 25 citations into it
• Aerospace Engineering — 14 citations into it

Whom Yongqiang Zhang 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. Shaoqing Ren
7. Karen Simonyan
8. Christian Szegedy
9. Sergey Ioffe
10. Piotr Doll'ar

Who trusts Yongqiang Zhang overall

1. Yancheng Bai
2. Ming Ding
3. Bernard Ghanem
4. Aditya Arun
5. Na Dong
6. Gong Cheng
7. Honggang Chen
8. Gao Yan
9. Poojan Oza
10. Zhiqiang Shen

Papers from Yongqiang Zhang's perspective

Ordered by how strongly the researchers Yongqiang Zhang 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. 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
• Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data (2023) — 8 citations
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (2021)
• 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
• Weakly-supervised object detection via mining pseudo ground truth bounding-boxes (2018) — 36 citations
• Finding Tiny Faces in the Wild with Generative Adversarial Network (2018) — 210 citations
• Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line (2017) — 13 citations