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Predicting ad click-through rates is the core problem in display advertising, which has received much attention from the machine learning community in recent years. In this paper, we present an online learning algorithm for click-though rate prediction, namely Follow-The-Regularized-Factorized-Leader (FTRFL), which incorporates the Follow-The-Regularized-Leader (FTRL-Proximal) algorithm with per-coordinate...
Multimedia annotation algorithms infer localized metadata in multimedia content, e.g. Speakers' voices or subjects' faces. There is a growing need of experts from this domain to perform advanced analyses, that go beyond medium-scale quality metrics. This paper describes a novel visual tool, that addresses the concerns of multimedia experts using interactive visualization principles. Multiple coordinated...
We address the problem of detecting multiple audiovisual events related to the edit structure of a video by incorporating an unsupervised cluster analysis technique into a cluster selection method designed to measure coherence between audio and visual segments. First, mutual information measure is used to select audio-visually consistent clusters from two dendrograms representing hierarchical clustering...
In this paper we tackle the problem of detecting individual human actions in video sequences. While the most successful methods are based on local features, which proved that they can deal with changes in background, scale and illumination, most existing methods have two main shortcomings: first, they are mainly based on the individual power of spatio-temporal interest points (STIP), and therefore...
Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal relations of the local features for action...
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