Tingting Qiao

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 536 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 83 citations into it; #9,498 of 21,575 by trust here, #8,021 by citations
• Artificial Intelligence — 39 citations into it; #21,576 of 35,345 by trust here, #25,040 by citations
• Generative Models — 38 citations into it; #722 of 1,956 by trust here, #865 by citations
• Multimodal Learning — 30 citations into it; #580 of 1,211 by trust here, #831 by citations
• Natural Language Processing — 16 citations into it
• Model Architectures — 9 citations into it
• Optimization & Training — 5 citations into it

Whom Tingting Qiao trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Christian Szegedy
10. Sepp Hochreiter

Who trusts Tingting Qiao overall

1. Jiadong Liang
2. Ming Tao
3. Wenjie Pei
4. Hongchen Tan
5. Tobias Hinz
6. Hui Ye
7. Feng Lu
8. Jing Zhang
9. Damien Teney
10. Yundong Zhang

Papers from Tingting Qiao's perspective

Ordered by how strongly the researchers Tingting Qiao 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• MirrorGAN: Learning Text-To-Image Generation by Redescription (2019) — 614 citations
• Exploring Human-like Attention Supervision in Visual Question Answering (2017) — 115 citations
• Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge (2019) — 104 citations