Attribute Learning for Understanding Unstructured Social Activity

Yanwei Fu, Timothy M. Hospedales, Tao Xiang, Shaogang Gong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

The rapid development of social video sharing platforms has created a huge demand for automatic video classification and annotation techniques, in particular for videos containing social activities of a group of people (e.g. YouTube video of a wedding reception). Recently, attribute learning has emerged as a promising paradigm for transferring learning to sparsely labelled classes in object or single-object short action classification. In contrast to existing work, this paper for the first time, tackles the problem of attribute learning for understanding group social activities with sparse labels. This problem is more challenging because of the complex multi-object nature of social activities, and the unstructured nature of the activity context. To solve this problem, we (1) contribute an unstructured social activity attribute (USAA) dataset with both visual and audio attributes, (2) introduce the concept of semi-latent attribute space and (3) propose a novel model for learning the latent attributes which alleviate the dependence of existing models on exact and exhaustive manual specification of the attribute-space. We show that our framework is able to exploit latent attributes to outperform contemporary approaches for addressing a variety of realistic multi-media sparse data learning tasks including: multi-task learning, N-shot transfer learning, learning with label noise and importantly zero-shot learning.
Original languageEnglish
Title of host publicationComputer Vision - ECCV 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV
PublisherSpringer Berlin Heidelberg
Pages530-543
Number of pages14
ISBN (Electronic)978-3-642-33765-9
ISBN (Print)978-3-642-33764-2
DOIs
Publication statusPublished - 2012

Publication series

NameLecture Notes in Computer Science (LNCS)
PublisherSpringer berlin Heidelberg
Volume7575
ISSN (Print)0302-9743

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