Ji Hou

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 409 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it; #12,800 of 21,575 by trust here, #11,721 by citations
• Computational Mechanics — 37 citations into it
• Geology — 30 citations into it; #372 of 687 by trust here, #311 by citations
• Aerospace Engineering — 27 citations into it; #2,954 of 6,421 by trust here, #2,245 by citations
• 3D Vision & Pose — 19 citations into it; #462 of 689 by trust here, #469 by citations
• Artificial Intelligence — 17 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Environmental Engineering — 11 citations into it

Whom Ji Hou trusts overall

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

1. Ross B. Girshick
2. Kaiming He
3. Angela Dai
4. Charles Ruizhongtai Qi
5. Shuran Song
6. Geoffrey E. Hinton
7. Alex Krizhevsky
8. Yoshua Bengio
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Ji Hou overall

1. Or Litany
2. Chen Liu
3. Charles Ruizhongtai Qi
4. Christopher Xie
5. Lei Han
6. Dávid Rozenberszki
7. Xiuwei Xu
8. Jonas Schult
9. Thang Vu
10. Li Jiang

Papers from Ji Hou's perspective

Ordered by how strongly the researchers Ji Hou 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. Long Short-Term Memory
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

• 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans (2018) — 548 citations
• Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts (2020) — 342 citations