Human Action Recognition with Hierarchical Growing Neural Gas Learning
By German Ignacio Parisi, Cornelius Weber, Stefan Wermter. 2014
22 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Cornelius Weber
- Jun Tani
- Vincenzo Lomonaco
- Christopher Kanan
- Tyler L. Hayes
In Computer Vision and Pattern Recognition
- German Ignacio Parisi
- Stefan Wermter
- Luiza Mici
- Jun Tani
- Cornelius Weber
- Vincenzo Lomonaco
- Christopher Kanan
- Tyler L. Hayes
Citations in the corpus
- A self-organizing neural network architecture for learning human-object interactions (2017, methodology): “Weadapted the original implementation of the GWR algorithm, which processes input data vectors in the spatial domain, to the processing of temporal data by the mechanism of the temporal sliding window [19].”
- Emergence of multimodal action representations from neural network self-organization (2017, methodology): “Our action dataset is composed of 10 full-body actions performed by 13 subjects (Parisi et al., 2014).”
- Self-organizing neural integration of pose-motion features for human action recognition (2015, methodology): “Similar to previously reported results (Parisi et al., 2014c), we evaluated the system on 30 sequences of 10s for each TABLE 1 | Training results on the two datasets—”