Xialei Liu

Computer Vision and Pattern Recognition · 2017–2023

A researcher in the Science corpus: 16 papers; trusts 1,853 researchers and is trusted by 73 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 609 citations into it; #3,423 of 21,575 by trust here, #1,584 by citations
• Artificial Intelligence — 529 citations into it; #4,410 of 35,345 by trust here, #2,301 by citations
• Meta & Continual Learning — 223 citations into it; #107 of 1,041 by trust here, #25 by citations
• Representation & Self-Supervised Learning — 189 citations into it; #2,156 of 3,145 by trust here, #2,454 by citations
• Optimization & Training — 72 citations into it
• Radiology, Nuclear Medicine and Imaging — 65 citations into it; #3,184 of 3,265 by trust here, #934 by citations
• Model Architectures — 63 citations into it
• Efficiency & Compression — 53 citations into it; #962 of 1,264 by trust here, #1,092 by citations

Whom Xialei Liu 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. Yann LeCun
8. Diederik P. Kingma
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Xialei Liu overall

1. Marc Masana
2. Joost van de Weijer
3. Da-Wei Zhou
4. Rahaf Aljundi
5. Liyuan Wang
6. Bartłomiej Twardowski
7. Andrew D. Bagdanov
8. Shagun Sodhani
9. Yaoyao Liu
10. Tinne Tuytelaars

Papers from Xialei Liu's perspective

Ordered by how strongly the researchers Xialei 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (2018) — 327 citations
• Semantic Drift Compensation for Class-Incremental Learning (2020) — 422 citations
• Memory Replay GANs: learning to generate images from new categories without forgetting (2018) — 203 citations
• Class-Incremental Learning: Survey and Performance Evaluation on Image Classification (2020) — 891 citations
• RankIQA: Learning from Rankings for No-Reference Image Quality Assessment (2017) — 516 citations
• Generative Feature Replay For Class-Incremental Learning (2020) — 190 citations
• Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (2018) — 307 citations
• Learning Metrics From Teachers: Compact Networks for Image Embedding (2019) — 123 citations
• Self-Training for Class-Incremental Semantic Segmentation (2020) — 70 citations
• Representation Compensation Networks for Continual Semantic Segmentation (2022) — 143 citations
• Endpoints Weight Fusion for Class Incremental Semantic Segmentation (2023) — 77 citations
• Long-Tailed Class Incremental Learning (2022) — 60 citations
• Improving Task Adaptation for Cross-domain Few-shot Learning (2021) — 14 citations
• Universal Representation Learning from Multiple Domains for Few-shot Classification (2021) — 115 citations
• Continual Universal Object Detection (2020) — 13 citations
• Multi-task incremental learning for object detection (2020) — 54 citations