no code implementations • 14 May 2023 • Matej Ulicny, Vladimir A. Krylov, Julie Connelly, Rozenn Dahyot
We propose a pipeline for combined multi-class object geolocation and height estimation from street level RGB imagery, which is considered as a single available input data modality.
1 code implementation • 22 Oct 2020 • Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain.
1 code implementation • 18 Jan 2020 • Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space.
Ranked #453 on Image Classification on ImageNet
1 code implementation • 30 Apr 2019 • Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
In the context of reverting to preset filters, we propose here a computationally efficient harmonic block that uses Discrete Cosine Transform (DCT) filters in CNNs.
Ranked #34 on Image Classification on STL-10
1 code implementation • 7 Dec 2018 • Matej Ulicny, Vladimir A. Krylov, Rozenn Dahyot
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space.
1 code implementation • 28 Aug 2017 • Vladimir A. Krylov, Eamonn Kenny, Rozenn Dahyot
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations.