Oscar Koller

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 677 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 109 citations into it; #7,106 of 21,575 by trust here, #9,722 by citations
• Artificial Intelligence — 82 citations into it
• Model Architectures — 47 citations into it
• Human-Computer Interaction — 44 citations into it; #350 of 2,253 by trust here, #437 by citations
• Natural Language Processing — 44 citations into it
• Developmental and Educational Psychology — 21 citations into it; #1,575 of 8,223 by trust here, #2,133 by citations
• Biomedical Engineering — 15 citations into it; #9,004 of 10,734 by trust here, #7,109 by citations
• Evaluation & Benchmarks — 15 citations into it

Whom Oscar Koller trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Christian Szegedy
7. Yann LeCun
8. Diederik P. Kingma
9. J. Urgen Schmidhuber
10. Kaiming He

Who trusts Oscar Koller overall

1. Necati Cihan Camgöz
2. Hamid Reza Vaezi Joze
3. Runpeng Cui
4. Simon Hadfield
5. Richard Bowden
6. Hu Liu
7. Jie Huang
8. Wen-gang Zhou
9. Changshui Zhang
10. Hao Zhou

Papers from Oscar Koller's perspective

Ordered by how strongly the researchers Oscar Koller 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Sign Language Transformers: Joint End-to-End Sign Language Recognition and Translation (2020) — 771 citations
• Neural Sign Language Translation (2018) — 863 citations
• Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition (2016) — 98 citations
• SubUNets: End-to-End Hand Shape and Continuous Sign Language Recognition (2017) — 335 citations
• MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language (2018) — 332 citations
• Weakly Supervised Learning with Multi-Stream CNN-LSTM-HMMs to Discover Sequential Parallelism in Sign Language Videos (2020) — 365 citations
• Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective (2019) — 457 citations
• Continuous sign language recognition: Towards large vocabulary statistical recognition systems handling multiple signers (2015) — 585 citations