Xiu-Shen Wei

Computer Vision and Pattern Recognition · 2015–2021

A researcher in the Science corpus: 12 papers; trusts 1,346 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 405 citations into it; #9,218 of 21,575 by trust here, #4,807 by citations
• Artificial Intelligence — 251 citations into it; #21,247 of 35,345 by trust here, #11,327 by citations
• Representation & Self-Supervised Learning — 65 citations into it
• Evaluation & Benchmarks — 41 citations into it
• Model Architectures — 40 citations into it
• Aerospace Engineering — 35 citations into it
• Optimization & Training — 29 citations into it
• Radiology, Nuclear Medicine and Imaging — 22 citations into it; #1,397 of 3,265 by trust here, #1,628 by citations

Whom Xiu-Shen Wei trusts overall

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

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

Who trusts Xiu-Shen Wei overall

1. Shiv Ram Dubey
2. Tao Hu
3. Chen-Lin Zhang
4. Honggang Qi
5. Bin Xiao
6. Yuxin Peng
7. Da-Wei Zhou
8. Heliang Zheng
9. Chris Dongjoo Kim
10. Jian-Hao Luo

Papers from Xiu-Shen Wei's perspective

Ordered by how strongly the researchers Xiu-Shen Wei 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
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

• Adversarial PoseNet: A Structure-Aware Convolutional Network for Human Pose Estimation (2017) — 348 citations
• BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition (2019) — 993 citations
• Weakly Supervised Fine-Grained Categorization With Part-Based Image Representation (2016) — 187 citations
• Multi-Label Image Recognition With Graph Convolutional Networks (2019) — 1,230 citations
• Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval (2016) — 442 citations
• Exploring Categorical Regularization for Domain Adaptive Object Detection (2020) — 341 citations
• Fine-Grained Image Analysis With Deep Learning: A Survey (2021) — 462 citations
• Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification (2021) — 298 citations
• Multi-Instance Learning With Emerging Novel Class (2021) — 31 citations
• Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization (2018) — 356 citations
• In Defense of Fully Connected Layers in Visual Representation Transfer (2017) — 85 citations
• Weakly Supervised Fine-Grained Image Categorization (2015) — 53 citations