Daqing Liu

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 3 papers; trusts 592 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 130 citations into it; #14,073 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 81 citations into it; #22,836 of 35,345 by trust here, #17,776 by citations
• Multimodal Learning — 43 citations into it
• Generative Models — 40 citations into it
• Natural Language Processing — 24 citations into it
• Model Architectures — 17 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Computer Graphics and Computer-Aided Design — 7 citations into it

Whom Daqing Liu trusts overall

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

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

Who trusts Daqing Liu overall

1. Zhengyuan Yang
2. Gen Luo
3. Ye Du
4. Xu Yang
5. Weicheng Kuo
6. Jiajun Deng
7. Swarat Chaudhuri
8. Tianlang Chen
9. Yiyi Zhou
10. Siyi Liu

Papers from Daqing Liu's perspective

Ordered by how strongly the researchers Daqing 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning to Assemble Neural Module Tree Networks for Visual Grounding (2018) — 319 citations
• Modeling Image Composition for Complex Scene Generation (2022) — 64 citations
• More Grounded Image Captioning by Distilling Image-Text Matching Model (2020) — 148 citations