Recognition of Transitive Actions with Hierarchical Neural Network Learning

By Luiza Mici, German Ignacio Parisi, Stefan Wermter. 2016

5 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. German Ignacio Parisi
2. Stefan Wermter
3. Luiza Mici
4. Cornelius Weber
5. Jun Tani
6. Vincenzo Lomonaco
7. Christopher Kanan
8. Tyler L. Hayes

In Human-Computer Interaction

1. Stefan Wermter
2. German Ignacio Parisi
3. Luiza Mici
4. Jun Tani
5. Cornelius Weber

Citations in the corpus

• An Incremental Self-Organizing Architecture for Sensorimotor Learning and Prediction (2017, background): “in time memory or recurrent connections [16], [17], [41], [42].”
• A self-organizing neural network architecture for learning human-object interactions (2017, positive): “On the basis of previously reported results in Mici et al. [21], this work contributes to improve the architecture design and provides a more in-depth analysis for an extended number of experiments.”