Xiao Bian

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 9 papers; trusts 1,095 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 446 citations into it; #8,757 of 21,575 by trust here, #2,456 by citations
• Artificial Intelligence — 170 citations into it; #16,382 of 35,345 by trust here, #5,638 by citations
• Evaluation & Benchmarks — 45 citations into it
• Aerospace Engineering — 40 citations into it
• Model Architectures — 29 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Radiology, Nuclear Medicine and Imaging — 19 citations into it
• Optimization & Training — 18 citations into it

Whom Xiao Bian 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. Karen Simonyan
6. Yoshua Bengio
7. Yann LeCun
8. Andrew Zisserman
9. Tsung-Yi Lin
10. Ilya Sutskever

Who trusts Xiao Bian overall

1. Shengkai Wu
2. Songtao Liu
3. Hugo Touvron
4. Fan Yang
5. Wei David Liu
6. Xiongwei Wu
7. Zhaowei Cai
8. Xue Yang
9. Xialei Liu
10. Peng Wang

Papers from Xiao Bian's perspective

Ordered by how strongly the researchers Xiao Bian 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
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

• The Visual Object Tracking VOT2015 Challenge Results (2015) — 901 citations
• Single-Shot Refinement Neural Network for Object Detection (2017) — 1,373 citations
• Vision Meets Drones: A Challenge (2018) — 533 citations
• Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd (2018) — 446 citations
• Feature Space Augmentation for Long-Tailed Data (2020) — 289 citations
• The Visual Object Tracking VOT2017 Challenge Results (2017) — 301 citations
• PseudoSeg: Designing Pseudo Labels for Semantic Segmentation (2020) — 376 citations
• VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results (2019) — 1,010 citations
• Detection and Tracking Meet Drones Challenge (2020) — 1,156 citations