Bo Xiong

Computer Vision and Pattern Recognition · with Fan · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 1,341 researchers and is trusted by 287 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 348 citations into it; #2,344 of 21,575 by trust here, #3,445 by citations
• Artificial Intelligence — 189 citations into it; #16,037 of 35,345 by trust here, #5,682 by citations
• Model Architectures — 85 citations into it; #1,298 of 2,278 by trust here, #576 by citations
• Representation & Self-Supervised Learning — 57 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Optimization & Training — 32 citations into it
• Multimodal Learning — 24 citations into it
• Signal Processing — 14 citations into it

Whom Bo Xiong trusts overall

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

1. Kaiming He
2. Ross B. Girshick
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Karen Simonyan
6. Andrew Zisserman
7. Yann LeCun
8. Yoshua Bengio
9. Diederik P. Kingma
10. Ilya Sutskever

Who trusts Bo Xiong overall

1. Wenguan Wang
2. Yanghao Li
3. Christoph Feichtenhofer
4. Ning Xu
5. Paul Voigtlaender
6. Haoqi Fan
7. Mennatullah Siam
8. Xiankai Lu
9. Hugo Touvron
10. Linjie Yang

Papers from Bo Xiong's perspective

Ordered by how strongly the researchers Bo Xiong 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. Attention is All you Need (Neural Information Processing Systems)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• Multiscale Vision Transformers (2021) — 1,678 citations
• MViTv2: Improved Multiscale Vision Transformers for Classification and Detection (2021) — 962 citations
• FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos (2017) — 398 citations
• Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos (2021) — 127 citations
• A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning (2021) — 302 citations