Xiaoxiao Li

Computer Vision and Pattern Recognition · with Loy · 2015–2020

A researcher in the Science corpus: 6 papers; trusts 1,135 researchers and is trusted by 748 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 212 citations into it; #1,579 of 21,575 by trust here, #1,284 by citations
• Artificial Intelligence — 105 citations into it; #5,006 of 35,345 by trust here, #1,813 by citations
• Model Architectures — 31 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Evaluation & Benchmarks — 17 citations into it
• Efficiency & Compression — 15 citations into it
• Aerospace Engineering — 10 citations into it
• Optimization & Training — 10 citations into it

Whom Xiaoxiao Li 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. Shaoqing Ren
7. Yann LeCun
8. Jia Deng
9. Yoshua Bengio
10. Diederik P. Kingma

Who trusts Xiaoxiao Li overall

1. Ho Kei Cheng
2. Liang-Chieh Chen
3. Wenguan Wang
4. Ziwei Liu
5. Kai Chen
6. Hengshuang Zhao
7. Changqian Yu
8. Paul Voigtlaender
9. Chunhua Shen
10. Seoung Wug Oh

Papers from Xiaoxiao Li's perspective

Ordered by how strongly the researchers Xiaoxiao Li 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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

• Semantic Image Segmentation via Deep Parsing Network (2015) — 671 citations
• Hybrid Task Cascade for Instance Segmentation (2019) — 1,537 citations
• MMDetection: Open MMLab Detection Toolbox and Benchmark (2019) — 3,471 citations
• Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade (2017) — 279 citations
• Video Object Segmentation with Joint Re-identification and Attention-Aware Mask Propagation (2018) — 206 citations
• Knowledge Distillation Meets Self-Supervision (2020) — 341 citations