Xianjie Chen

Computer Vision and Pattern Recognition · with Yuille · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 944 researchers and is trusted by 3,127 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 127 citations into it; #650 of 21,575 by trust here, #1,551 by citations
• Artificial Intelligence — 48 citations into it
• Human-Computer Interaction — 10 citations into it
• Information Systems — 10 citations into it
• 3D Vision & Pose — 8 citations into it
• Model Architectures — 7 citations into it; #285 of 2,278 by trust here, #827 by citations
• Biomedical Engineering — 6 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Xianjie Chen trusts overall

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

1. Ross B. Girshick
2. Pedro F. Felzenszwalb
3. Alex Krizhevsky
4. Geoffrey E. Hinton
5. Navneet Dalal
6. Kaiming He
7. Andrew Zisserman
8. Ilya Sutskever
9. Yann LeCun
10. Deva Ramanan

Who trusts Xianjie Chen overall

1. Alan L. Yuille
2. Quanshi Zhang
3. Liang-Chieh Chen
4. Iasonas Kokkinos
5. Juergen Gall
6. Wanli Ouyang
7. Xiao Chu
8. Xiaodan Liang
9. Zhishuai Zhang
10. Xiaogang Wang

Papers from Xianjie Chen's perspective

Ordered by how strongly the researchers Xianjie Chen 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. 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
• Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations (2014) — 530 citations
• Parsing occluded people by flexible compositions (2014) — 81 citations
• DeePM: A Deep Part-Based Model for Object Detection and Semantic Part Localization (2015) — 23 citations
• Deep Learning Recommendation Model for Personalization and Recommendation Systems (2019) — 944 citations