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Found 18 papers, 9 papers with code
Combining geolocation and height estimation of objects from street level imagery
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.
Model-based inexact graph matching on top of CNNs for semantic scene understanding
On FASSEG data, results show that our module improves accuracy of the CNN by about 6. 3% (the Hausdorff distance decreases from 22. 11 to 20. 71).
Principal Component Classification
We propose to directly compute classification estimates by learning features encoded with their class scores using PCA.
DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet
Accurate retinal vessel segmentation is an important task for many computer-aided diagnosis systems.
Context Aware Object Geotagging
Localization of street objects from images has gained a lot of attention in recent years.
3D point cloud segmentation using GIS
In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap).
Sliced $\mathcal{L}_2$ Distance for Colour Grading
We propose a new method with $\mathcal{L}_2$ distance that maps one $N$-dimensional distribution to another, taking into account available information about correspondences.
Tensor Reordering for CNN Compression
We show how parameter redundancy in Convolutional Neural Network (CNN) filters can be effectively reduced by pruning in spectral domain.
Iterative Nadaraya-Watson Distribution Transfer for Colour Grading
We propose a new method with Nadaraya-Watson that maps one N-dimensional distribution to another taking into account available information about correspondences.
Patch based Colour Transfer using SIFT Flow
We propose a new colour transfer method with Optimal Transport (OT) to transfer the colour of a sourceimage to match the colour of a target image of the same scene that may exhibit large motion changes betweenimages.
Harmonic Convolutional Networks based on Discrete Cosine Transform
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space.
Ranked #452 on
Image Classification
on ImageNet
Performance-Oriented Neural Architecture Search
Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems.
Entropic Regularisation of Robust Optimal Transport
Grogan et al [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target).
Harmonic Networks with Limited Training Samples
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
Harmonic Networks: Integrating Spectral Information into CNNs
Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space.
Automatic Discovery and Geotagging of Objects from Street View Imagery
Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations.
Shape Registration with Directional Data
We propose several cost functions for registration of shapes encoded with Euclidean and/or non-Euclidean information (unit vectors).
Robust Registration of Gaussian Mixtures for Colour Transfer
We present a flexible approach to colour transfer inspired by techniques recently proposed for shape registration.
