Mengde Xu

Computer Vision and Pattern Recognition · 2019–2023

A researcher in the Science corpus: 4 papers; trusts 634 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 151 citations into it; #21,550 of 21,575 by trust here, #6,542 by citations
• Artificial Intelligence — 100 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Model Architectures — 17 citations into it
• Optimization & Training — 7 citations into it

Whom Mengde Xu trusts overall

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

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

Who trusts Mengde Xu overall

1. Xiangtai Li
2. Zeyu Han
3. Yanbei Chen
4. Xingyi Zhou
5. Lihe Yang
6. Dat T. Huynh
7. Jongheon Jeong
8. Henghui Ding
9. Liunian Harold Li
10. Jianwei Yang

Papers from Mengde Xu's perspective

Ordered by how strongly the researchers Mengde Xu 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• End-to-End Semi-Supervised Object Detection with Soft Teacher (2021) — 662 citations
• Asymmetric Non-Local Neural Networks for Semantic Segmentation (2019) — 705 citations
• Side Adapter Network for Open-Vocabulary Semantic Segmentation (2023) — 461 citations
• A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-Language Model (2021) — 346 citations