L'exploitation romaine du sel dans la region de Sigúenza

Sin resume

Co-Regularized Deep Representations for Video Summarization

Compact keyframe-based video summaries are a popular way of generating viewership on video sharing platforms. Yet, creating relevant and compelling summaries for arbitrarily long videos with a small number of keyframes is a challenging task. We propose a comprehensive keyframe-based summarization framework combining deep convolutional neural networks and restricted Boltzmann machines. An original co-regularization scheme is used to discover meaningful subject-scene associations. The resulting multimodal representations are then used to select highly-relevant keyframes. A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites. The results show that our method consistently outperforms the baseline schemes for any given amount of keyframes both in terms of attractiveness and informativeness. The lead is even more significant for smaller summaries.Comment: Video summarization, deep convolutional neural networks, co-regularized restricted Boltzmann machine

Group Invariant Deep Representations for Image Instance Retrieval

Most image instance retrieval pipelines are based on comparison of vectors known as global image descriptors between a query image and the database images. Due to their success in large scale image classification, representations extracted from Convolutional Neural Networks (CNN) are quickly gaining ground on Fisher Vectors (FVs) as state-of-the-art global descriptors for image instance retrieval. While CNN-based descriptors are generally remarked for good retrieval performance at lower bitrates, they nevertheless present a number of drawbacks including the lack of robustness to common object transformations such as rotations compared with their interest point based FV counterparts. In this paper, we propose a method for computing invariant global descriptors from CNNs. Our method implements a recently proposed mathematical theory for invariance in a sensory cortex modeled as a feedforward neural network. The resulting global descriptors can be made invariant to multiple arbitrary transformation groups while retaining good discriminativeness. Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated. We also show that our method which has few parameters is not prone to overfitting: improvements generalize well across datasets with different properties with regard to invariances. Finally, we show that our descriptors are able to compare favourably to other state-of-the-art compact descriptors in similar bitranges, exceeding the highest retrieval results reported in the literature on some datasets. A dedicated dimensionality reduction step --quantization or hashing-- may be able to further improve the competitiveness of the descriptors

Simultaneous Parameters Identification and State Estimation based on Unknown Input Observer for a class of LPV Systems

International audienceA novel unknown input observer structure for parameters and state estimation is proposed to enhance the performance of the estimator. In this paper, we suggest how a failed matching condition in a nonlinear unknown input observer can be recovered by using time delayed measurement to solve the inversing problem. Based on delayed outputs, an augmented system is constructed from which the parameters of the model and the system states can be simultaneously estimated. The augmented nonlinear model is transformed into a Takagi Sugeno (TS) form. Sufficient conditions for the existence of the estimator are given in terms of linear matrix inequalities (LMIs). Using the obtained information on the unknown input observer, unknown parameters are identified. Finally, the feasibility and the effectiveness of the suggested approach is demonstrated on examples

Avant-propos

L’étrange, le mystérieux et le surnaturel font partie des clichés qui resurgissent sans cesse à propos de l’Écosse. C’est la raison pour laquelle le centre de recherches de Grenoble, en plus de vingt d’ans d’existence, a volontairement jusqu’ici exclu de ses travaux cet aspect de la culture écossaise, préférant se concentrer sur la pensée du xviiie siècle, sur le renouveau de la littérature contemporaine ainsi que sur de récents développements politiques. Si le présent numéro d’Études écossai..

Nigel Leask (ed.), The Oxford Edition of the Works of Robert Burns

Ce premier volume magistral des presses d’Oxford sur l’œuvre de Robert Burns comprend les Commonplace Books, les Tour Journals, et Miscellaneous Prose. Les amateurs des Burns Suppers et fervents un peu crédules du heaven taught ploughman, du barde inspiré autodidacte, seront sans doute un peu déçus de voir leur héros, non pas descendu de son piédestal, loin de là, mais débarrassé du folklore qui l’entoure, bien que la légende ait été entretenue par le poète lui-même. Ce volume n’est pas un re..

Bernard Sellin (éd.), Voices from Modern Scotland: Janice Galloway, Alasdair Gray

Ce livre, rédigé en anglais et particulièrement stimulant, rassemble plusieurs articles présentés un premier temps sous forme de communications lors d’un colloque tenu à l’université de Nantes en 2006. Tous concourent à démontrer le tournant majeur qu’a pris la littérature écossaise contemporaine depuis la publication de Lanark d’Alasdair Gray en 1981. On lira avec intérêt, après l’introduction de Bernard Sellin, les articles de Marie-Odile Pittin-Hédon : « “For God’s Sake, Don’t Beleive What..