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.
no code implementations • 4 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).
1 code implementation • CVPR 2019 • Subhankar Roy, Aliaksandr Siarohin, Enver Sangineto, Samuel Rota Bulo, Nicu Sebe, Elisa Ricci
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
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.
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.
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.
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).
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.
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.