Dan Xu

Computer Vision and Pattern Recognition · 2015–2022

A researcher in the Science corpus: 12 papers; trusts 1,525 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 430 citations into it; #6,524 of 21,575 by trust here, #4,193 by citations
• Artificial Intelligence — 308 citations into it; #22,316 of 35,345 by trust here, #10,319 by citations
• Natural Language Processing — 75 citations into it
• Model Architectures — 67 citations into it
• Representation & Self-Supervised Learning — 58 citations into it
• Aerospace Engineering — 45 citations into it
• Media Technology — 43 citations into it; #1,010 of 3,621 by trust here, #515 by citations
• Information Systems — 32 citations into it

Whom Dan Xu trusts overall

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

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

Who trusts Dan Xu overall

1. Ibraheem Alhashim
2. Radu Tudor Ionescu
3. Shariq Farooq Bhat
4. Peter Wonka
5. Jingwen Ye
6. Xiangtai Li
7. Simon Vandenhende
8. Zhenyu Zhang
9. Mariana-Iuliana Georgescu
10. Weixin Luo

Papers from Dan Xu's perspective

Ordered by how strongly the researchers Dan Xu 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. Attention is All you Need (Neural Information Processing Systems)
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

• PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing (2018) — 571 citations
• Learning Deep Representations of Appearance and Motion for Anomalous Event Detection (2015) — 541 citations
• Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction (2017) — 134 citations
• Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation (2018) — 321 citations
• DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection (2022) — 265 citations
• Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation (2017) — 444 citations
• Detecting anomalous events in videos by learning deep representations of appearance and motion (2017) — 464 citations
• Multi-class Token Transformer for Weakly Supervised Semantic Segmentation (2022) — 302 citations
• Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (2022) — 97 citations
• Delving into Localization Errors for Monocular 3D Object Detection (2021) — 294 citations
• Every Smile is Unique: Landmark-Guided Diverse Smile Generation (2018) — 71 citations
• Neural information retrieval: at the end of the early years (2017) — 138 citations