1,623 research outputs found

    Affect Recognition in Ads with Application to Computational Advertising

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    Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

    Segmentation de scènes extérieures à partir d'ensembles d'étiquettes à granularité et sémantique variables

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    International audienceIn this work, we present an approach that leverages multiple datasets annotated using different classes (different labelsets) to improve the classification accuracy on each individual dataset. We focus on semantic full scene labeling of outdoor scenes. To achieve our goal, we use the KITTI dataset as it illustrates very well the focus of our paper : it has been sparsely labeled by multiple research groups over the past few years but the semantics and the granularity of the labels differ from one set to another. We propose a method to train deep convolutional networks using multiple datasets with potentially inconsistent labelsets and a selective loss function to train it with all the available labeled data while being reliant to inconsistent labelings. Experiments done on all the KITTI dataset's labeled subsets show that our approach consistently improves the classification accuracy by exploiting the correlations across data-sets both at the feature level and at the label level.Ce papier présente une approche permettant d'utiliser plusieurs bases de données annotées avec différents ensembles d'étiquettes pour améliorer la précision d'un classifieur entrainé sur une tâche de segmentation sémantique de scènes extérieures. Dans ce contexte, la base de données KITTI nous fournit un cas d'utilisation particulièrement pertinent : des sous-ensembles distincts de cette base ont été annotés par plusieurs équipes en utilisant des étiquettes différentes pour chaque sous-ensemble. Notre méthode permet d'entraîner un réseau de neurones convolutionnel (CNN) en utilisant plusieurs bases de données avec des étiquettes possiblement incohérentes. Nous présentons une fonction de perte sélective pour entrainer ce réseau et plusieurs approches de fusion permettant d'exploiter les corrélations entre les différents ensembles d'étiquettes. Le réseau utilise ainsi toutes les données disponibles pour améliorer les performances de classification sur chaque ensemble. Les expériences faites sur les différents sous-ensembles de la base de données KITTI montrent comment chaque proposition contribue à améliorer le classifieur

    Binary Classification with Positive Labeling Sources

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    To create a large amount of training labels for machine learning models effectively and efficiently, researchers have turned to Weak Supervision (WS), which uses programmatic labeling sources rather than manual annotation. Existing works of WS for binary classification typically assume the presence of labeling sources that are able to assign both positive and negative labels to data in roughly balanced proportions. However, for many tasks of interest where there is a minority positive class, negative examples could be too diverse for developers to generate indicative labeling sources. Thus, in this work, we study the application of WS on binary classification tasks with positive labeling sources only. We propose WEAPO, a simple yet competitive WS method for producing training labels without negative labeling sources. On 10 benchmark datasets, we show WEAPO achieves the highest averaged performance in terms of both the quality of synthesized labels and the performance of the final classifier supervised with these labels. We incorporated the implementation of \method into WRENCH, an existing benchmarking platform.Comment: CIKM 2022 (short

    Object Localization, Segmentation, and Classification in 3D Images

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    We address the problem of identifying objects of interest in 3D images as a set of related tasks involving localization of objects within a scene, segmentation of observed object instances from other scene elements, classifying detected objects into semantic categories, and estimating the 3D pose of detected objects within the scene. The increasing availability of 3D sensors motivates us to leverage large amounts of 3D data to train machine learning models to address these tasks in 3D images. Leveraging recent advances in deep learning has allowed us to develop models capable of addressing these tasks and optimizing these tasks jointly to reduce potential errors propagated when solving these tasks independently
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