Xiaoyu Lin

Computer Vision and Pattern Recognition · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 856 researchers and is trusted by 146 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 137 citations into it; #2,908 of 21,575 by trust here, #6,606 by citations
• Artificial Intelligence — 116 citations into it; #6,338 of 35,345 by trust here, #9,369 by citations
• Natural Language Processing — 51 citations into it
• Multimodal Learning — 48 citations into it; #293 of 1,211 by trust here, #696 by citations
• Evaluation & Benchmarks — 23 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Cognitive Neuroscience — 14 citations into it
• Model Architectures — 8 citations into it

Whom Xiaoyu Lin trusts overall

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

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

Who trusts Xiaoyu Lin overall

1. Licheng Yu
2. Qi Wu
3. Luowei Zhou
4. Rowan Zellers
5. Linchao Zhu
6. Eunbyung Park
7. Yunseok Jang
8. Aishwarya Agrawal
9. Devi Parikh
10. Yuke Zhu

Papers from Xiaoyu Lin's perspective

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

Their papers

• Don't just listen, use your imagination: Leveraging visual common sense for non-visual tasks (2015) — 68 citations
• Learning Common Sense through Visual Abstraction (2015) — 92 citations
• Leveraging Visual Question Answering for Image-Caption Ranking (2016) — 88 citations
• Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts (2019) — 116 citations
• Active Learning for Visual Question Answering: An Empirical Study (2017) — 35 citations
• Data-Efficient Mutual Information Neural Estimator (2019) — 25 citations