Xiaobai Liu

Computer Vision and Pattern Recognition · 2012–2017

A researcher in the Science corpus: 6 papers; trusts 370 researchers and is trusted by 1,216 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 114 citations into it; #1,219 of 21,575 by trust here, #1,948 by citations
• Artificial Intelligence — 43 citations into it
• Computational Mechanics — 36 citations into it
• Optimization & Training — 24 citations into it
• Statistics and Probability — 13 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Biomedical Engineering — 6 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Xiaobai Liu trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Pedro F. Felzenszwalb
6. Andrew Zisserman
7. Yoshua Bengio
8. Yann LeCun
9. Diederik P. Kingma
10. Antonio Torralba

Who trusts Xiaobai Liu overall

1. Iasonas Kokkinos
2. Alan L. Yuille
3. Quanshi Zhang
4. Liang-Chieh Chen
5. Bolei Zhou
6. Chunhua Shen
7. Xiaodan Liang
8. Bharath Hariharan
9. Yunchao Wei
10. Guosheng Lin

Papers from Xiaobai Liu's perspective

Ordered by how strongly the researchers Xiaobai Liu 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. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• The Role of Context for Object Detection and Semantic Segmentation in the Wild (2014) — 1,654 citations
• Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts (2014) — 748 citations
• Visual Classification With Multitask Joint Sparse Representation (2012) — 410 citations
• Stochastic Gradient Made Stable: A Manifold Propagation Approach for Large-Scale Optimization (2015) — 36 citations
• Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation (2017) — 391 citations
• Learning Collaborative Sparse Representation for Grayscale-Thermal Tracking (2016) — 356 citations