Gerhard Rigoll

Computer Vision and Pattern Recognition · 2003–2021

A researcher in the Science corpus: 10 papers; trusts 61 researchers and is trusted by 42 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 44 citations into it; #5,552 of 21,575 by trust here, #8,952 by citations
• Artificial Intelligence — 38 citations into it; #20,063 of 35,345 by trust here, #19,873 by citations
• Signal Processing — 24 citations into it; #1,271 of 7,572 by trust here, #1,783 by citations
• Model Architectures — 17 citations into it
• Biomedical Engineering — 15 citations into it
• Natural Language Processing — 12 citations into it
• Experimental and Cognitive Psychology — 7 citations into it; #1,333 of 9,731 by trust here, #3,491 by citations
• Aerospace Engineering — 6 citations into it

Whom Gerhard Rigoll trusts overall

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

1. Chris Stauffer
2. Olivier Barnich
3. Alex Graves
4. Geoffrey E. Hinton
5. Marc Van Droogenbroeck
6. Sergey Ioffe
7. Iroon Polytechniou
8. Björn Schuller
9. Yoshua Bengio
10. Arthur P. Dempster

Who trusts Gerhard Rigoll overall

1. Thierry Bouwmans
2. Björn Schuller
3. Christos-Nikolaos E. Anagnostopoulos
4. Moataz M. H. El Ayadi
5. Weiyan Shi
6. Nandita Sharma
7. Dimitrios Ververidis
8. Andrews Cordolino Sobral
9. Kun Han
10. Theodoros Iliou

Papers from Gerhard Rigoll's perspective

Ordered by how strongly the researchers Gerhard Rigoll trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Deep Convolutional Neural Network for Background Subtraction (2017) — 88 citations
• Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling (2014) — 75 citations
• Gaitgraph: Graph Convolutional Network for Skeleton-Based Gait Recognition (2021) — 308 citations
• A deep convolutional neural network for video sequence background subtraction (2018) — 381 citations
• The MERL/MELCO/TUM system for the REVERB Challenge using Deep Recurrent Neural Network Feature Enhancement (2014) — 44 citations
• Keyword spotting exploiting Long Short-Term Memory (2013) — 56 citations
• Background segmentation with feedback: The Pixel-Based Adaptive Segmenter (2012) — 586 citations
• Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture (2004) — 484 citations
• SVC2004: First International Signature Verification Competition (2004) — 500 citations
• Hidden Markov model-based speech emotion recognition (2003) — 693 citations