no code implementations • • 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.
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).
A classifier trained on a dataset seldom works on other datasets obtained under different conditions due to domain shift.
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.
Randomized classification trees are among the most popular machine learning tools and found successful applications in many areas.
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.
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).
In this work we present Neural Decision Forests, a novel approach to jointly tackle data representation- and discriminative learning within randomized decision trees.
We propose a shape matching method that produces dense correspondences tuned to a specific class of shapes and deformations.