Bei Liu

Computer Vision and Pattern Recognition · with Fu · 2020–2022

A researcher in the Science corpus: 7 papers; trusts 825 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 246 citations into it; #5,585 of 21,575 by trust here, #3,982 by citations
• Artificial Intelligence — 185 citations into it; #7,865 of 35,345 by trust here, #5,565 by citations
• Multimodal Learning — 160 citations into it; #278 of 1,211 by trust here, #358 by citations
• Natural Language Processing — 60 citations into it
• Evaluation & Benchmarks — 39 citations into it
• Generative Models — 37 citations into it
• Model Architectures — 32 citations into it
• Representation & Self-Supervised Learning — 31 citations into it

Whom Bei Liu trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Yoshua Bengio
6. Ross B. Girshick
7. Ilya Sutskever
8. Yann LeCun
9. Shaoqing Ren
10. Jimmy Ba

Who trusts Bei Liu overall

1. Zhe Gan
2. Jianfeng Wang
3. Zi-Yi Dou
4. Yupan Huang
5. Linjie Li
6. Hongwei Xue
7. Zhengyuan Yang
8. Jaemin Cho
9. Xiaowei Hu
10. Yan Zeng

Papers from Bei Liu's perspective

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

• Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformers (2020) — 475 citations
• Seeing Out of tHe bOx: End-to-End Pre-training for Vision-Language Representation Learning (2021) — 313 citations
• Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training (2021) — 100 citations
• M3P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training (2021) — 120 citations
• CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Alignment (2022) — 115 citations
• MM-Diffusion: Learning Multi-Modal Diffusion Models for Joint Audio and Video Generation (2022) — 308 citations
• Unifying Multimodal Transformer for Bi-directional Image and Text Generation (2021) — 67 citations