Alexander Hermans
Computer Vision and Pattern Recognition · 2014–2022
A researcher in the Science corpus: 8 papers; trusts 1,447 researchers and is trusted by 907 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 305 citations into it; #1,444 of 21,575 by trust here, #2,214 by citations
- Artificial Intelligence — 89 citations into it; #19,965 of 35,345 by trust here, #10,319 by citations
- Aerospace Engineering — 53 citations into it; #1,314 of 6,421 by trust here, #2,656 by citations
- Model Architectures — 40 citations into it
- Computational Mechanics — 35 citations into it; #8,743 of 10,001 by trust here, #6,041 by citations
- Geology — 27 citations into it; #597 of 687 by trust here, #568 by citations
- 3D Vision & Pose — 24 citations into it
- Evaluation & Benchmarks — 23 citations into it
Whom Alexander Hermans trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Geoffrey E. Hinton
- Alex Krizhevsky
- Ross B. Girshick
- Diederik P. Kingma
- Yann LeCun
- Yoshua Bengio
- Ilya Sutskever
- Karen Simonyan
- Christian Szegedy
Who trusts Alexander Hermans overall
- Mang Ye
- Liang Zheng
- Zhun Zhong
- Zhedong Zheng
- Arsha Nagrani
- Yi Yang
- Vijay Badrinarayanan
- Guan'an Wang
- Aliaksandr Siarohin
- Jian Zhao
Papers from Alexander Hermans's perspective
Ordered by how strongly the researchers Alexander Hermans trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- In Defense of the Triplet Loss for Person Re-Identification (2017) — 3,600 citations
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features (2017) — 368 citations
- Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes (2016) — 589 citations
- Dense 3D semantic mapping of indoor scenes from RGB-D images (2014) — 282 citations
- Multi-scale object candidates for generic object tracking in street scenes (2016) — 44 citations
- Mask3D: Mask Transformer for 3D Semantic Instance Segmentation (2022) — 409 citations
- Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds (2017) — 281 citations
- Superpixels: An evaluation of the state-of-the-art (2016) — 520 citations