The PASCAL Visual Object Classes Challenge

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.

In Defense of the Triplet Loss for Person Re-Identification

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

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Context-Aware Recommender Systems

The importance of contextual information has been recognized by researchers and practitioners in many disciplines, including e-commerce personalization, information retrieval, ubiquitous and mobile computing, data mining, marketing, and management. While a substantial amount of research has already been performed in the area of recommender systems, most existing approaches focus on recommending the most relevant items to users without taking into account any additional contextual information, such as time, location, or the company of other people (e.g., for watching movies or dining out). In this chapter we argue that relevant contextual information does matter in recommender systems and that it is important to take this information into account when providing recommendations. We discuss the general notion of context and how it can be modeled in recommender systems. Furthermore, we introduce three different algorithmic paradigms – contextual prefiltering, post-filtering, and modeling – for incorporating contextual information into the recommendation process, discuss the possibilities of combining several contextaware recommendation techniques into a single unifying approach, and provide a case study of one such combined approach. Finally, we present additional capabilities for context-aware recommenders and discuss important and promising directions for future research.

/pdf/context-aware-recommender-systems-8uash1fwm4.pdf

Context-aware recommender systems

Exploiting temporal context has been proved to be an effective approach to improve recommendation performance, as shown, e.g. in the Netflix Prize competition. Time-aware recommender systems (TARS) are indeed receiving increasing attention. A wide range of approaches dealing with the time dimension in user modeling and recommendation strategies have been proposed. In the literature, however, reported results and conclusions about how to incorporate and exploit time information within the recommendation processes seem to be contradictory in some cases. Aiming to clarify and address existing discrepancies, in this paper we present a comprehensive survey and analysis of the state of the art on TARS. The analysis show that meaningful divergences appear in the evaluation protocols used--metrics and methodologies. We identify a number of key conditions on offline evaluation of TARS, and based on these conditions, we provide a comprehensive classification of evaluation protocols for TARS. Moreover, we propose a methodological description framework aimed to make the evaluation process fair and reproducible. We also present an empirical study on the impact of different evaluation protocols on measuring relative performances of well-known TARS. The results obtained show that different uses of the above evaluation conditions yield to remarkably distinct performance and relative ranking values of the recommendation approaches. They reveal the need of clearly stating the evaluation conditions used to ensure comparability and reproducibility of reported results. From our analysis and experiments, we finally conclude with methodological issues a robust evaluation of TARS should take into consideration. Furthermore we provide a number of general guidelines to select proper conditions for evaluating particular TARS.

/pdf/time-aware-recommender-systems-a-comprehensive-survey-and-40qfzy0zif.pdf

Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols

In this paper, we introduce a framework for modular generation of context-aware recommendations. The components of this framework include context sensors, recommender algorithms and utility modules (converters and lters), all realized as so-called services, which can

/pdf/context-aware-recommendations-on-rails-introducing-a-context-1risewyu2q.pdf

Context-aware recommendations on rails Introducing a context-aware recommender construction kit

Einfluss des Verwandtschaftsgrades bei positiven Familienanamnese für Typ-2-Diabetes als Risikofaktor für Schwangerschaftsdiabetes

The invention relates to a method for predicting (30) at least one future velocity vector and/or a future pose of a pedestrian in an area of prediction. A map of the surroundings of the pedestrian and current velocity vectors of other pedestrians in the area of prediction are taken into account for the prediction (30).

Method for predicting at least one future velocity vector and/or a future pose of a pedestrian

We tackle deconvolution of motion blur in hand-held consumer photography with a Bayesian framework combing sparse gradient and color priors for regularization. We develop a closed-form optimization utilizing iterated re-weighted least squares (IRLS) with a Gaussian approximation of the regularization priors. The model parameters of the priors can be learned from a set of natural images because they resemble common image statistics. We throughly evaluate and discuss the effect of different regularization factors and make suggestions for reasonable values. Both gradient and color priors are current state-of-the-art. In natural images the magnitude of gradients resembles a kurtotic hyper-Laplacian distribution, and the two-color model exploits the observation that locally any color is a linear approximation between some primary and secondary colors. Our contribution is integrating both priors into a single optimization framework and providing a more detailed derivation of their optimization functions. Our re-implementation reveals different model parameters than previously published, and the effectiveness of the color priors alone are explicitly examined. Finally, we propose a context-adaptive parameterization of the regularization factors in order to avoid over-smoothing the deconvolution result in highly textured areas.

http://www.maschinensehen.de/herwig14icpram.pdf

Timm Linder

Papers

Context-aware recommendations on rails Introducing a context-aware recommender construction kit

Einfluss des Verwandtschaftsgrades bei positiven Familienanamnese für Typ-2-Diabetes als Risikofaktor für Schwangerschaftsdiabetes

Method for predicting at least one future velocity vector and/or a future pose of a pedestrian

Removing Motion Blur using Natural Image Statistics