Zhongang Qi

Computer Vision and Pattern Recognition · 2018–2023

A researcher in the Science corpus: 6 papers; trusts 546 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 225 citations into it; #18,155 of 21,575 by trust here, #7,750 by citations
• Generative Models — 100 citations into it
• Artificial Intelligence — 70 citations into it
• Multimodal Learning — 60 citations into it
• Computational Mechanics — 28 citations into it; #2,588 of 10,001 by trust here, #2,447 by citations
• Statistical and Nonlinear Physics — 20 citations into it
• Geology — 15 citations into it; #185 of 687 by trust here, #255 by citations
• Model Architectures — 15 citations into it

Whom Zhongang Qi trusts overall

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

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

Who trusts Zhongang Qi overall

1. Xu Yan
2. Shaoshuai Shi
3. Ishan Misra
4. Lin Zhao
5. Yongming Rao
6. Saifullahi Aminu Bello
7. Rohit Girdhar
8. Zaiwei Zhang
9. Freyr Sverrisson
10. Benjamin Ummenhofer

Papers from Zhongang Qi's perspective

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

Their papers

• T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models (2023) — 1,776 citations
• Open-book Video Captioning with Retrieve-Copy-Generate Network (2021) — 118 citations
• LayoutDiffusion: Controllable Diffusion Model for Layout-to-Image Generation (2023) — 352 citations
• PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embedding (2023) — 389 citations
• MasaCtrl: Tuning-Free Mutual Self-Attention Control for Consistent Image Synthesis and Editing (2023) — 846 citations
• PointConv: Deep Convolutional Networks on 3D Point Clouds (2018) — 1,894 citations