R. Cowie
Experimental and Cognitive Psychology · AVEC@ACM Multimedia · 2000–2019
A researcher in the Science corpus: 17 papers; trusts 38 researchers and is trusted by 40 researchers here, as inferred from the citation graph.
Subfields
- Experimental and Cognitive Psychology — 84 citations into it; #674 of 9,731 by trust here, #437 by citations
- Artificial Intelligence — 62 citations into it; #25,904 of 35,345 by trust here, #17,412 by citations
- Social Psychology — 52 citations into it; #1,802 of 15,694 by trust here, #1,584 by citations
- Computer Vision and Pattern Recognition — 44 citations into it
- Cognitive Neuroscience — 34 citations into it; #2,907 of 10,852 by trust here, #2,465 by citations
- Signal Processing — 34 citations into it; #3,093 of 7,572 by trust here, #1,202 by citations
- Evaluation & Benchmarks — 10 citations into it
- Cognitive Science & Psychology — 5 citations into it
Whom R. Cowie trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ellen Douglas-Cowie
- R. Reisenzein
- Florian Eyben
- Björn Schuller
- Klaus R. Scherer
- Anton Batliner
- Charu C. Aggarwal
- Tim Dalgleish
- Chih-Jen Lin
- Paul Ekman
Who trusts R. Cowie overall
- Björn Schuller
- Dimitrios Kollias
- Hatice Gunes
- Ellen Douglas-Cowie
- Mihalis A. Nicolaou
- Michel François Valstar
- Carlos Busso
- Marc Schröder
- Maja Pantic
- Stefanos P. Zafeiriou
Papers from R. Cowie's perspective
Ordered by how strongly the researchers R. Cowie 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)
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- Et al
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- ImageNet: A large-scale hierarchical image database
Their papers
- Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies (2008) — 317 citations
- Describing the emotional states that are expressed in speech (2003) — 792 citations
- The SEMAINE corpus of emotionally coloured character interactions (2010) — 322 citations
- The HUMAINE Database: Addressing the Collection and Annotation of Naturalistic and Induced Emotional Data (2007) — 366 citations
- Emotion representation, analysis and synthesis in continuous space: A survey (2011) — 326 citations
- AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge (2017) — 361 citations
- Emotional speech: Towards a new generation of databases (2003) — 513 citations
- Emotion recognition in human-computer interaction (2001) — 618 citations
- AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition (2019) — 420 citations
- The ordinal nature of emotions (2017) — 106 citations
- AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge (2016) — 679 citations
- AVEC 2014: 3D Dimensional Affect and Depression Recognition Challenge (2014) — 543 citations
- AVEC 2013: the continuous audio/visual emotion and depression recognition challenge (2013) — 619 citations
- AVEC 2012: the continuous audio/visual emotion challenge (2012) — 305 citations
- AVEC 2011-The First International Audio/Visual Emotion Challenge (2011) — 314 citations
- FEELTRACE: an instrument for recording perceived emotion in real time (2000) — 674 citations
- A new emotion database: considerations, sources and scope (2000) — 202 citations