Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. The source code and model used in this paper are publicly available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September 201

Simple vs complex temporal recurrences for video saliency prediction

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a conceptually simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps. Source code is available at https://git.io/fjPiB

The same and the contrary. Construction of a paradox in the Visitors Center of the Swiss National Park by Valerio Olgiati

[EN] Making a riddle is to establish an estrangement to solve. Its time tends to be a closed time, finished, complete. Anyway, if the riddle eludes the solution, its value will stay in the permanency of the paradox. Its time will be an open time, in a continuous course. The Visitors Center of Zemez is a perpetual estrangement: the outside and the inside are the same and the contrary at the same time. The contrary because the symmetrical and stable exterior don’t make us predict the labyrinthine inside. The same because there is continuity and a simple and complete unity between the inside and the outside achieved by the use of a single monolithic and unifying constructive system: walls and frameworks of white reinforced concrete. This article will describe how, with the use of this material, the construction of a radical and authentic estrangement is expected.[ES] Construir un acertijo es establecer un extrañamiento a resolver. Su tiempo tiende a ser un tiempo cerrado, acabado, completo. Ahora bien, si el acertijo elude la solución, su valor residirá en la permanencia de la paradoja. Su tiempo será un tiempo abierto, en continua circulación. El Centro de Visitantes de Zernez es un extrañamiento perpetuo: el exterior y el interior son a la vez lo mismo y lo contrario. Lo contrario porque el exterior simétrico y estable no nos hace predecir su laberíntico interior. Lo mismo porque existe una continuidad y una unidad simple y total entre el dentro y el fuera conseguida por la utilización de un único sistema constructivo monolítico e integrador: muros y forjados de hormigón armado blanco. El presente escrito describe cómo con la utilización de este material se pretende la construcción de un extrañamiento radical y auténtico.Nieto García, JÁ. (2014). Lo mismo y lo contrario Construcción de una paradoja en el Centro de Visitantes del Parque Nacional Suizo de Valerio Olgiati. EN BLANCO. Revista de Arquitectura. 6(16):87-93. doi:10.4995/eb.2014.5813SWORD8793616CORTÁZAR, Julio. "Después del almuerzo" en: Final de juego, Buenos Aires, Editorial Sudamericana, 1964, 194 p

Scalable Inference of Gene Regulatory Networks with the Spark Distributed Computing Platform Cristo

Inference of Gene Regulatory Networks (GRNs) remains an important open challenge in computational biology. The goal of bio-model inference is to, based on time-series of gene expression data, obtain the sparse topological structure and the parameters that quantitatively understand and reproduce the dynamics of biological system. Nevertheless, the inference of a GRN is a complex optimization problem that involve processing S-System models, which include large amount of gene expression data from hundreds (even thousands) of genes in multiple time-series (essays). This complexity, along with the amount of data managed, make the inference of GRNs to be a computationally expensive task. Therefore, the genera- tion of parallel algorithmic proposals that operate efficiently on distributed processing platforms is a must in current reconstruction of GRNs. In this paper, a parallel multi-objective approach is proposed for the optimal inference of GRNs, since min- imizing the Mean Squared Error using S-System model and Topology Regularization value. A flexible and robust multi-objective cellular evolutionary algorithm is adapted to deploy parallel tasks, in form of Spark jobs. The proposed approach has been developed using the framework jMetal, so in order to perform parallel computation, we use Spark on a cluster of distributed nodes to evaluate candidate solutions modeling the interactions of genes in biological networks.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines

The present work is intended for residual oxide scale detection and classification through the application of image processing techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

¿Por qué no escuchan los presidentes?

Los depositarios del ejercicio del supremo poder ejecutivo de la Unión, nombre que confiere el artículo 80 de la Constitución Política de los Estados Unidos Mexicanos a los presidentes de la república, están obligados a escuchar al pueblo. Durante su campaña para ocupar tal cargo y mientras lo desempeñan se desgañitan para enarbolar el compromiso de hacerlo. Sin embargo, el pueblo se considera poco atendido aun expresándose a gritos. Un cúmulo exorbitante de tareas intrascendentes consumen un tiempo considerable de las actividades presidenciales, muchas destinadas a incrementar sus niveles de popularidad y a ¿cuidar su imagen¿. A la omnipresencia presidencial se suma también el cerco de colaboradores, familiares, amigos y poderosos. Por ende, hay descuido de tareas fundamentales para los habitantes del país. Desafortunadamente hasta ahora sólo cuando el pueblo se rebela se hace escuchar por los gobernantes. La eliminación de los factores silenciadores del pueblo permitirá escuchar su voz libre para asegurar su dignidad fundada en su mejoramiento económico, social y cultural