Di Xie

Computer Vision and Pattern Recognition · International Joint Conference on Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 7 papers; trusts 464 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 232 citations into it; #7,479 of 21,575 by trust here, #8,296 by citations
• Artificial Intelligence — 108 citations into it; #26,651 of 35,345 by trust here, #19,439 by citations
• Model Architectures — 34 citations into it
• Biomedical Engineering — 29 citations into it; #2,299 of 10,734 by trust here, #6,773 by citations
• Efficiency & Compression — 28 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Optimization & Training — 14 citations into it
• Human-Computer Interaction — 13 citations into it; #668 of 2,253 by trust here, #1,221 by citations

Whom Di Xie trusts overall

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

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

Who trusts Di Xie overall

1. Yisheng Song
2. Zhang Zhang
3. Lei Shi
4. Fanfan Ye
5. Fida Mohammad Thoker
6. Yifan Zhang

Papers from Di Xie's perspective

Ordered by how strongly the researchers Di Xie 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Unsupervised Image Classification for Deep Representation Learning (2020) — 14 citations
• Skeleton-based action recognition with convolutional neural networks (2017) — 417 citations
• Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019) — 337 citations
• Extreme Network Compression via Filter Group Approximation (2018) — 67 citations
• Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition (2020) — 348 citations
• Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation (2018) — 579 citations
• A Practical Convolutional Neural Network as Loop Filter for Intra Frame (2018) — 42 citations