Xiaoyu Tao

Computer Vision and Pattern Recognition · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 564 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 177 citations into it; #5,126 of 21,575 by trust here, #4,105 by citations
• Artificial Intelligence — 155 citations into it; #6,784 of 35,345 by trust here, #5,084 by citations
• Representation & Self-Supervised Learning — 69 citations into it
• Meta & Continual Learning — 47 citations into it; #176 of 1,041 by trust here, #121 by citations
• Efficiency & Compression — 22 citations into it
• Optimization & Training — 21 citations into it
• Radiology, Nuclear Medicine and Imaging — 16 citations into it; #975 of 3,265 by trust here, #1,183 by citations
• Signal Processing — 16 citations into it

Whom Xiaoyu Tao trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Ross B. Girshick
5. Yoshua Bengio
6. Ilya Sutskever
7. Shaoqing Ren
8. Yann LeCun
9. Jia Deng
10. Oriol Vinyals

Who trusts Xiaoyu Tao overall

1. Da-Wei Zhou
2. Tyler L. Hayes
3. Liyuan Wang
4. Songlin Dong
5. Xiaopeng Hong
6. Han-Jia Ye
7. Xinyuan Chang
8. Hanbin Zhao
9. Kuilin Chen
10. Christopher Kanan

Papers from Xiaoyu Tao's perspective

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

Their papers

• Few-Shot Class-Incremental Learning (2020) — 556 citations
• Topology-Preserving Class-Incremental Learning (2020) — 203 citations
• Transductive Semi-Supervised Deep Learning Using Min-Max Features (2018) — 240 citations
• Few-Shot Class-Incremental Learning via Relation Knowledge Distillation (2021) — 224 citations
• Class Incremental Learning for Video Action Classification (2021) — 8 citations
• Class-Incremental Learning with Topological Schemas of Memory Spaces (2021) — 5 citations
• Analogy-Detail Networks for Object Recognition (2020) — 9 citations
• Bi-Objective Continual Learning: Learning 'New' While Consolidating 'Known' (2020) — 31 citations