21,355 research outputs found
Social relation recognition in egocentric photostreams
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental’s social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.Peer ReviewedPostprint (author's final draft
Social Relation Recognition in Egocentric Photostreams
This paper proposes an approach to automatically categorize the social
interactions of a user wearing a photo-camera 2fpm, by relying solely on what
the camera is seeing. The problem is challenging due to the overwhelming
complexity of social life and the extreme intra-class variability of social
interactions captured under unconstrained conditions. We adopt the
formalization proposed in Bugental's social theory, that groups human relations
into five social domains with related categories. Our method is a new deep
learning architecture that exploits the hierarchical structure of the label
space and relies on a set of social attributes estimated at frame level to
provide a semantic representation of social interactions. Experimental results
on the new EgoSocialRelation dataset demonstrate the effectiveness of our
proposal.Comment: Accepted at ICIP 201
Pinterest Board Recommendation for Twitter Users
Pinboard on Pinterest is an emerging media to engage online social media
users, on which users post online images for specific topics. Regardless of its
significance, there is little previous work specifically to facilitate
information discovery based on pinboards. This paper proposes a novel pinboard
recommendation system for Twitter users. In order to associate contents from
the two social media platforms, we propose to use MultiLabel classification to
map Twitter user followees to pinboard topics and visual diversification to
recommend pinboards given user interested topics. A preliminary experiment on a
dataset with 2000 users validated our proposed system
DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging
Tagging news articles or blog posts with relevant tags from a collection of
predefined ones is coined as document tagging in this work. Accurate tagging of
articles can benefit several downstream applications such as recommendation and
search. In this work, we propose a novel yet simple approach called DocTag2Vec
to accomplish this task. We substantially extend Word2Vec and Doc2Vec---two
popular models for learning distributed representation of words and documents.
In DocTag2Vec, we simultaneously learn the representation of words, documents,
and tags in a joint vector space during training, and employ the simple
-nearest neighbor search to predict tags for unseen documents. In contrast
to previous multi-label learning methods, DocTag2Vec directly deals with raw
text instead of provided feature vector, and in addition, enjoys advantages
like the learning of tag representation, and the ability of handling newly
created tags. To demonstrate the effectiveness of our approach, we conduct
experiments on several datasets and show promising results against
state-of-the-art methods.Comment: 10 page
Knowledge management, innovation and big data: Implications for sustainability, policy making and competitiveness
This Special Issue of Sustainability devoted to the topic of “Knowledge Management, Innovation and Big Data: Implications for Sustainability, Policy Making and Competitiveness” attracted exponential attention of scholars, practitioners, and policy-makers from all over the world. Locating themselves at the expanding cross-section of the uses of sophisticated information and communication technology (ICT) and insights from social science and engineering, all papers included in this Special Issue contribute to the opening of new avenues of research in the field of innovation, knowledge management, and big data. By triggering a lively debate on diverse challenges that companies are exposed to today, this Special Issue offers an in-depth, informative, well-structured, comparative insight into the most salient developments shaping the corresponding fields of research and policymaking
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