Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB

Early chronic kidney disease: diagnosis, management and models of care

Chronic kidney disease (CKD) is prevalent in many countries, and the costs associated with the care of patients with end-stage renal disease (ESRD) are estimated to exceed US$1 trillion globally. The clinical and economic rationale for the design of timely and appropriate health system responses to limit the progression of CKD to ESRD is clear. Clinical care might improve if early-stage CKD with risk of progression to ESRD is differentiated from early-stage CKD that is unlikely to advance. The diagnostic tests that are currently used for CKD exhibit key limitations; therefore, additional research is required to increase awareness of the risk factors for CKD progression. Systems modelling can be used to evaluate the impact of different care models on CKD outcomes and costs. The US Indian Health Service has demonstrated that an integrated, system-wide approach can produce notable benefits on cardiovascular and renal health outcomes. Economic and clinical improvements might, therefore, be possible if CKD is reconceptualized as a part of primary care. This Review discusses which early CKD interventions are appropriate, the optimum time to provide clinical care, and the most suitable model of care to adopt

MetalGAN: a Cluster-based Adaptive Training for Few-Shot Adversarial Colorization

In recent years, the majority of works on deep-learning-based image colorization have focused on how to make a good use of the enormous datasets currently available. What about when the data at disposal are scarce? The main objective of this work is to prove that a network can be trained and can provide excellent colorization results even without a large quantity of data. The adopted approach is a mixed one, which uses an adversarial method for the actual colorization, and a meta-learning technique to enhance the generator model. Also, a clusterization a-priori of the training dataset ensures a task-oriented division useful for metalearning, and at the same time reduces the per-step number of images. This paper describes in detail the method and its main motivations, and a discussion of results and future developments is provided

European governments on the future of development cooperation

Der Bericht umfasst drei Vortraege bzw. Stellungnahmen von VertreterInnen der Regierungen Grossbritanniens, Frankreichs und Schwedens zur zukuenftigen Entwicklungszusammenarbeit. Die britische Staatssekretaerin im Kabinettsrang fuer Internationale Entwicklung aeussert sich zur Entwicklungszusammenarbeit (ihres Landes) im Kontext einer sich globalisierenden Welt. Der beigeordnete Minister fuer Entwicklungszusammenarbeit und Frankophonie aus Frankreich setzt sich mit der Frage auseinander, wie eine europaeische Politik fuer Entwicklungszusammenarbeit aussehen sollte. Der schwedische Minister fuer Internationale Entwicklungszusammenarbeit geht in seinen Ausfuehrungen der Frage nach, wie man der Herausforderung der Globalisierung begegnet. (ICG2)German title: Europaeische Regierungen zur Zukunft der EntwicklungszusammenarbeitSIGLEAvailable from Friedrich-Ebert-Stiftung e.V., Bonn (DE) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman