Search Results for author: Samuel Rota Bulo

Found 9 papers, 1 papers with code

OrienterNet: Visual Localization in 2D Public Maps with Neural Matching

no code implementations CVPR 2023 Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang, Armen Avetisyan, Julian Straub, Tomasz Malisiewicz, Samuel Rota Bulo, Richard Newcombe, Peter Kontschieder, Vasileios Balntas

We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the same 2D semantic maps that humans use.

Visual Localization

Shape Consistent 2D Keypoint Estimation under Domain Shift

no code implementations4 Aug 2020 Levi O. Vasconcelos, Massimiliano Mancini, Davide Boscaini, Samuel Rota Bulo, Barbara Caputo, Elisa Ricci

Recent unsupervised domain adaptation methods based on deep architectures have shown remarkable performance not only in traditional classification tasks but also in more complex problems involving structured predictions (e. g. semantic segmentation, depth estimation).

Depth Estimation Keypoint Estimation +2

The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes

no code implementations ICCV 2017 Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, Peter Kontschieder

The Mapillary Vistas Dataset is a novel, large-scale street-level image dataset containing 25, 000 high-resolution images annotated into 66 object categories with additional, instance-specific labels for 37 classes.

Image Segmentation Instance Segmentation +3

Online Learning With Bayesian Classification Trees

no code implementations CVPR 2016 Samuel Rota Bulo, Peter Kontschieder

Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas.

BIG-bench Machine Learning Classification +1

Deep Neural Decision Forests

no code implementations ICCV 2015 Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulo

We present Deep Neural Decision Forests - a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner.

Representation Learning

Uncovering Interactions and Interactors: Joint Estimation of Head, Body Orientation and F-Formations From Surveillance Videos

no code implementations ICCV 2015 Elisa Ricci, Jagannadan Varadarajan, Ramanathan Subramanian, Samuel Rota Bulo, Narendra Ahuja, Oswald Lanz

We present a novel approach for jointly estimating tar- gets' head, body orientations and conversational groups called F-formations from a distant social scene (e. g., a cocktail party captured by surveillance cameras).


Neural Decision Forests for Semantic Image Labelling

no code implementations CVPR 2014 Samuel Rota Bulo, Peter Kontschieder

In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees.

Representation Learning

Dense Non-Rigid Shape Correspondence using Random Forests

no code implementations CVPR 2014 Emanuele Rodola, Samuel Rota Bulo, Thomas Windheuser, Matthias Vestner, Daniel Cremers

We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations.

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