Jens Behley
Computer Vision and Pattern Recognition · 2012–2022
A researcher in the Science corpus: 8 papers; trusts 522 researchers and is trusted by 26 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 122 citations into it; #11,884 of 21,575 by trust here, #5,941 by citations
- Aerospace Engineering — 95 citations into it; #879 of 6,421 by trust here, #845 by citations
- Geology — 53 citations into it; #155 of 687 by trust here, #347 by citations
- Computational Mechanics — 40 citations into it
- Environmental Engineering — 28 citations into it; #800 of 5,479 by trust here, #792 by citations
- Evaluation & Benchmarks — 26 citations into it
- 3D Vision & Pose — 24 citations into it
- Artificial Intelligence — 20 citations into it
Whom Jens Behley trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andreas Geiger
- Sebastian Thrun
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Ross B. Girshick
- Andrew E. Johnson
- Ji Zhang
- Andrew Zisserman
- Yann LeCun
Who trusts Jens Behley overall
- Bo Li
- Xieyuanli Chen
- Xu Yan
- Haoyang Ye
- Ignacio Vizzo
- Daniel Maturana
- Andres Milioto
- Mohsen Yavartanoo
- Yiming Li
- Yunpeng Zhang
Papers from Jens Behley's perspective
Ordered by how strongly the researchers Jens Behley trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet Large Scale Visual Recognition Challenge
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) — 2,459 citations
- Laser-based segment classification using a mixture of bag-of-words (2013) — 100 citations
- ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals (2019) — 397 citations
- RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation (2019) — 1,313 citations
- KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way (2022) — 550 citations
- SuMa++: Efficient LiDAR-based Semantic SLAM (2019) — 555 citations
- Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments (2018) — 527 citations
- Performance of histogram descriptors for the classification of 3D laser range data in urban environments (2012) — 111 citations