Tobias Hinz

Computer Vision and Pattern Recognition · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 1,045 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 234 citations into it; #5,720 of 21,575 by trust here, #8,021 by citations
• Generative Models — 148 citations into it; #1,504 of 1,956 by trust here, #1,315 by citations
• Artificial Intelligence — 104 citations into it
• Multimodal Learning — 65 citations into it; #549 of 1,211 by trust here, #962 by citations
• Evaluation & Benchmarks — 33 citations into it
• Natural Language Processing — 25 citations into it
• Optimization & Training — 24 citations into it
• Model Architectures — 18 citations into it

Whom Tobias Hinz trusts overall

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

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

Who trusts Tobias Hinz overall

1. Jaemin Cho
2. Stanislav Frolov
3. Han Zhang
4. Zejian Li
5. Yufan Zhou
6. Tianfu Wu
7. Abhaysinh Zala
8. Jiadong Liang
9. Tingting Qiao
10. Jing Yu Koh

Papers from Tobias Hinz's perspective

Ordered by how strongly the researchers Tobias Hinz 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

• Generating Multiple Objects at Spatially Distinct Locations (2019) — 107 citations
• Semantic Object Accuracy for Generative Text-to-Image Synthesis (2019) — 188 citations
• SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model (2022) — 379 citations
• Adversarial Text-to-Image Synthesis: A Review (2021) — 207 citations
• Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks (2018) — 111 citations