120 research outputs found
Learning to count with deep object features
Learning to count is a learning strategy that has been recently proposed in
the literature for dealing with problems where estimating the number of object
instances in a scene is the final objective. In this framework, the task of
learning to detect and localize individual object instances is seen as a harder
task that can be evaded by casting the problem as that of computing a
regression value from hand-crafted image features. In this paper we explore the
features that are learned when training a counting convolutional neural network
in order to understand their underlying representation. To this end we define a
counting problem for MNIST data and show that the internal representation of
the network is able to classify digits in spite of the fact that no direct
supervision was provided for them during training. We also present preliminary
results about a deep network that is able to count the number of pedestrians in
a scene.Comment: This paper has been accepted at Deep Vision Workshop at CVPR 201
Corridor Detection from Large GPS Trajectories Datasets
Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urbanmetropolitan area,we propose a algorithmto detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs
Estimand-Agnostic Causal Query Estimation with Deep Causal Graphs
Causal Queries are usually estimated by means of an estimand, a formula consisting of observational terms that can be computed using passive data. Each query results in a different formula, which makes estimand-based methods extremely ad-hoc. In this work, we propose an estimand-agnostic framework capable of computing any identifiable causal query on an arbitrary Causal Graph (even in the presence of latent confounders) with only one general model. We provide multiple implementations of this general framework that leverage the expressive power of Neural Networks and Normalizing Flows to model complex distributions, and we derive estimation procedures for all kinds of observational, interventional and counterfactual queries, valid for any kind of graph for which the query is identifiable. Finally, we test our techniques in a modelling setting and an estimation benchmark to show how, despite being a query-agnostic framework, it can compete with query-specific models. Our proposal includes an open-source library that allows easy application and extension of our techniques for researchers and practitioners alike
¿Qué sabe de usted su robot aspirador?
Durante los últimos años, uno de los productos más vendidos durante las jornadas de rebajas conocidas como Black Friday o Cyber Monday ha sido el robot aspirador. Sus avanzadas prestaciones, que permiten su puesta en marcha y funcionamiento cuando el usuario está ausente de su domicilio, han seducido de forma clara a los compradores.
Pero este éxito de ventas ha venido acompañado también de algunas dudas: ¿están estos robots adquiriendo datos sensibles que pueden ser vendidos en el mercado global de los datos personales
Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR data
Airborne LiDAR systems have the capability to capture the Earth's surface by
generating extensive point cloud data comprised of points mainly defined by 3D
coordinates. However, labeling such points for supervised learning tasks is
time-consuming. As a result, there is a need to investigate techniques that can
learn from unlabeled data to significantly reduce the number of annotated
samples. In this work, we propose to train a self-supervised encoder with
Barlow Twins and use it as a pre-trained network in the task of semantic scene
segmentation. The experimental results demonstrate that our unsupervised
pre-training boosts performance once fine-tuned on the supervised task,
especially for under-represented categories.Comment: International conference Machine Vision Applications 202
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