End-to-end weakly-supervised semantic alignment

We tackle the task of semantic alignment where the goal is to compute dense semantic correspondence aligning two images depicting objects of the same category. This is a challenging task due to large intra-class variation, changes in viewpoint and background clutter. We present the following three principal contributions. First, we develop a convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs. The outcome is that parameters are learnt from rich appearance variation present in different but semantically related images without the need for tedious manual annotation of correspondences at training time. Second, the main component of this architecture is a differentiable soft inlier scoring module, inspired by the RANSAC inlier scoring procedure, that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter. Third, we demonstrate that the proposed approach achieves state-of-the-art performance on multiple standard benchmarks for semantic alignment.Comment: In 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018

Convolutional neural network architecture for geometric matching

We address the problem of determining correspondences between two images in agreement with a geometric model such as an affine or thin-plate spline transformation, and estimating its parameters. The contributions of this work are three-fold. First, we propose a convolutional neural network architecture for geometric matching. The architecture is based on three main components that mimic the standard steps of feature extraction, matching and simultaneous inlier detection and model parameter estimation, while being trainable end-to-end. Second, we demonstrate that the network parameters can be trained from synthetically generated imagery without the need for manual annotation and that our matching layer significantly increases generalization capabilities to never seen before images. Finally, we show that the same model can perform both instance-level and category-level matching giving state-of-the-art results on the challenging Proposal Flow dataset.Comment: In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

Neighbourhood Consensus Networks

We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding scene elements and ambiguities generated by repetitive patterns. The contributions of this work are threefold. First, inspired by the classic idea of disambiguating feature matches using semi-local constraints, we develop an end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model. Second, we demonstrate that the model can be trained effectively from weak supervision in the form of matching and non-matching image pairs without the need for costly manual annotation of point to point correspondences. Third, we show the proposed neighbourhood consensus network can be applied to a range of matching tasks including both category- and instance-level matching, obtaining the state-of-the-art results on the PF Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS 2018

D2-Net: A Trainable CNN for Joint Detection and Description of Local Features

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level structures. We show that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations. The proposed method obtains state-of-the-art performance on both the difficult Aachen Day-Night localization dataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarks for image matching and 3D reconstruction.Comment: Accepted at CVPR 201

Conversión de residuos sólidos urbanos en energía

Given Uruguay’s energetic situation, where its main energy sources are from hydroelectric and thermal power from fossil fuels, it is important to consider other energy sources such as Energy from Waste. Waste to Energy (WTE) or Energy from Waste is a Municipal Solid Waste (MSW) management system, which results in an appropriate and sustainable use of the waste which cannot be efficiently recycled or reused. This articleoverviews the different WTE alternatives and proposes a concrete small-scale application for the city of Paysandú.Ante la situación energética del Uruguay, donde sus principales fuentes energéticas son a través de centrales hidráulicas y centrales térmicas a partir de combustibles fósiles, es importante considerar otras fuentes de energía como ser el recurso energético de los residuos. Recuperación de Energía de los Residuos o Waste-to-Energy (WTE), por su sigla en inglés, es un sistema de gestión para la disposición de los residuos sólidos urbanos (MSW, Municipal Solid Waste) que permite un uso adecuado y sostenible de los residuos que no pueden ser eficientemente reciclados o reutilizados. El presente artículo presenta las diferentes alternativas existentes para generar energía a partir de residuos y propone un caso concreto de aplicación de pequeña escala, utilizando un Sistema de Oxidación por Batch para la ciudad de Paysandú, Uruguay

Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories

Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors. Earlier works on sparse rigid object reconstruction successfully learned such priors from large datasets such as CO3D. In this paper, we extend this approach to dynamic objects. We use cats and dogs as a representative example and introduce Common Pets in 3D (CoP3D), a collection of crowd-sourced videos showing around 4,200 distinct pets. CoP3D is one of the first large-scale datasets for benchmarking non-rigid 3D reconstruction "in the wild". We also propose Tracker-NeRF, a method for learning 4D reconstruction from our dataset. At test time, given a small number of video frames of an unseen object, Tracker-NeRF predicts the trajectories of its 3D points and generates new views, interpolating viewpoint and time. Results on CoP3D reveal significantly better non-rigid new-view synthesis performance than existing baselines

The psychological benefits and efficacy of computer-assisted training on competency enhancement in adults with intellectual disability. A systematic review

The use of modern technologies as instructional tools is becoming increasingly prevalent in both general and special education. This systematic review examines the effects of computer-assisted and digital training on competency enhancement for adults with intellectual disabilities. As opposed to mere knowledge, “competency” was defined as the ability to apply and employ acquired knowledge to carry out tasks and solve problems in professional, social, and personal life domains. The PRISMA procedure was used to search for records dealing with competency enhancement using computerized training for intellectually disabled adults. Twenty-two articles met the inclusion criteria, showing that contemporary technology, computer-based tools, and digital instruments can positively affect the quality of life of adults with intellectual disabilities, and enhance their personal, professional, and social competencies. Ultimately, fostering computer-based technology to enhance competencies in adults with intellectual disabilities appears to be very promising, in that it allows these individuals to better integrate into society and live more independently, autonomously, and effectively

Desarrollo de modelos discretos aplicados al estudio del comportamiento en fractura de materiales compuestos

En el presente trabajo se propone estudiar el comportamiento en fractura de los materiales compuestos. Las ventajas que presentan los materiales compuestos, frente a los materiales tradicionales, ha permitido extender su uso a una amplia variedad de industrias, donde se incluye la automotor, militar, aeroespacial y naval, caracterizándose por la tendencia a ir sustituyendo componentes estructurales fabricados con materiales tradicionales. Por estos motivos, es importante poder desarrollar herramientas que permitan estudiar el comportamiento mecánico de estos materiales, y así poder optimizar el diseño de los componentes estructurales.Universidad Nacional de La Plat

Sensibilidad a la fisuración por coacciones térmicas y retracción hidráulica a edad temprana en tabiques de hormigón

En las obras hidráulicas destinadas a la contención o transporte de aguas, tales como son las plantas de tratamiento, estaciones de bombeo y conductos, un requisito muy importante es poder asegurar que los elementos estructurales que componen la obra cumplan con ciertos requisitos de estanqueidad y de durabilidad establecidos en los reglamentos de diseño y los estipulados por el proyectista. En el caso particular de estructuras de hormigón armado el cumplimiento de tales requisitos conlleva a la necesidad de tener que asegurar la impermeabilidad de las juntas y fijar ciertos límites a la fisuración del hormigón. En relación con este último aspecto es muy común que no se admitan anchos de fisura mayores que 0,1 mm o 0,2 mm dependiendo del tirante hidráulico y del espesor de la estructura. Atendiendo a estas limitaciones es importante que a nivel del proyecto ejecutivo se realicen análisis de fisuración que no solo incluyan a las solicitaciones impuestas por las cargas externas sino también las originadas por las coacciones térmicas debidas al calor de hidratación del cemento y la retracción hidráulica del hormigón. Atendiendo a esta necesidad en el presente trabajo se presenta un análisis numérico, aplicando el Método de los Elementos Finitos, con el objetivo de estudiar la sensibilidad a la fisuración a edad temprana debida al calor de hidratación del cemento y la contracción autógena en tabiques de hormigón. Los resultados de las simulaciones realizadas permiten cuantificar la influencia que el tipo de cemento, tipo de encofrado y época de hormigonado tienen sobre los requerimientos de armadura mínima necesaria para poder garantizar determinados anchos de fisura. Para la simulación se han tenido en cuenta las condiciones ambientales típicas imperantes en CABA y zona de influencia.Facultad de Ingenierí

Desarrollo de modelos discretos aplicados al estudio del comportamiento en fractura de materiales compuestos

En el presente trabajo se propone estudiar el comportamiento en fractura de los materiales compuestos. Las ventajas que presentan los materiales compuestos, frente a los materiales tradicionales, ha permitido extender su uso a una amplia variedad de industrias, donde se incluye la automotor, militar, aeroespacial y naval, caracterizándose por la tendencia a ir sustituyendo componentes estructurales fabricados con materiales tradicionales. Por estos motivos, es importante poder desarrollar herramientas que permitan estudiar el comportamiento mecánico de estos materiales, y así poder optimizar el diseño de los componentes estructurales.Universidad Nacional de La Plat