PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks

By Daniel Nyga, Ferenc Bálint-Benczédi, Michael Beetz. 2014

43 citations recorded by the source corpus.

Subfields: Aerospace Engineering, 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 Aerospace Engineering

1. Luciano Serafini
2. Oliver Lemon
3. Ivan Donadello
4. A.S. d'Avila Garcez

In Artificial Intelligence

1. Luciano Serafini
2. A.S. d'Avila Garcez
3. Ivan Donadello
4. Oliver Lemon
5. L. C. Lamb
6. Michael Spranger
7. Verena Rieser
8. Son Dinh Tran

In Computer Vision and Pattern Recognition

1. A.S. d'Avila Garcez
2. Luciano Serafini
3. Ivan Donadello
4. Oliver Lemon
5. Gesina Schwalbe
6. Bettina Finzel

Citations in the corpus

• Logic Tensor Networks for Semantic Image Interpretation (2017, methodology): “In [Zhu et al., 2014; Nyga et al., 2014], visual features are integrated with knowledge (gathered from datasets, Web resources or annotators) about object labels, properties (e.g. shape, colour, size) and affordances (what an object is useful for) using Markov Logic Network (MLN) [Richardson and…”
• Incremental On-Line Learning of Object Classes using a Combination of Self-Organizing Incremental Neural Networks and Deep Convolutional Neural Networks* (2016, background): “Nyga et al. [10] used an ensemble of experts coupled with a Markov Logic Network (MLN) to classify common household objects.”
• Semantic Image Interpretation - Integration of Numerical Data and Logical Knowledge for Cognitive Vision (2018, unknown): “In [113, 73], visual features are combined with knowledge gathered from datasets, web resources or annotators, about object labels, properties (for example, shape, colour, size) and affordances, using a Markov Logic Network (MLN) [84].”