Maxim Neumann

Computer Vision and Pattern Recognition · 2019–2024

A researcher in the Science corpus: 8 papers; trusts 507 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 261 citations into it; #20,326 of 21,575 by trust here, #4,137 by citations
• Artificial Intelligence — 205 citations into it; #34,006 of 35,345 by trust here, #7,010 by citations
• Representation & Self-Supervised Learning — 83 citations into it
• Evaluation & Benchmarks — 53 citations into it
• Model Architectures — 43 citations into it
• Generative Models — 34 citations into it
• Optimization & Training — 27 citations into it
• Media Technology — 24 citations into it

Whom Maxim Neumann trusts overall

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

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

Who trusts Maxim Neumann overall

1. Oscar Mañas
2. Yezhen Cong
3. Stefano Vincenzi
4. Danfeng Hong
5. Colorado Reed
6. Alexandre Lacoste

Papers from Maxim Neumann's perspective

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

Their papers

• A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark (2019) — 558 citations
• Simple Open-Vocabulary Object Detection with Vision Transformers (2022) — 421 citations
• Scaling Vision with Sparse Mixture of Experts (2021) — 1,110 citations
• Simple Open-Vocabulary Object Detection (2022) — 437 citations
• In-domain representation learning for remote sensing (2019) — 78 citations
• PaliGemma: A versatile 3B VLM for transfer (2024) — 728 citations
• AttentionNAS: Spatiotemporal Attention Cell Search for Video Classification (2020) — 54 citations
• The Visual Task Adaptation Benchmark (2019) — 129 citations