Xiangteng He

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 1,066 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 142 citations into it; #7,071 of 21,575 by trust here, #9,563 by citations
• Artificial Intelligence — 65 citations into it; #21,475 of 35,345 by trust here, #22,463 by citations
• Aerospace Engineering — 23 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Multimodal Learning — 21 citations into it; #521 of 1,211 by trust here, #935 by citations
• Model Architectures — 18 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Developmental Biology — 7 citations into it

Whom Xiangteng He trusts overall

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

1. Karen Simonyan
2. Andrew Zisserman
3. Christian Szegedy
4. David G. Lowe
5. Geoffrey E. Hinton
6. Sergey Ioffe
7. Alex Krizhevsky
8. Ross B. Girshick
9. Kaiming He
10. Yann LeCun

Who trusts Xiangteng He overall

1. Krishna Kumar Singh
2. Hua Zhang
3. Sarah Pratt
4. Xiu-Shen Wei
5. Tianshui Chen
6. Peiqin Zhuang
7. Tristan Sylvain
8. Utkarsh Ojha
9. Xiaochun Cao
10. Rosanne Liu

Papers from Xiangteng He's perspective

Ordered by how strongly the researchers Xiangteng He 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

• Fine-Grained Image Classification via Combining Vision and Language (2017) — 184 citations
• Object-Part Attention Model for Fine-Grained Image Classification (2017) — 379 citations
• Weakly Supervised Learning of Part Selection Model with Spatial Constraints for Fine-Grained Image Classification (2017) — 95 citations
• HANet: Hierarchical Alignment Networks for Video-Text Retrieval (2021) — 76 citations