Concepts not alone: Exploring pairwise relationships for zero-shot video activity recognition

Gan, C; Lin, M; Yang, Y; De Melo, G; Hauptmann, AG

Concepts not alone: Exploring pairwise relationships for zero-shot video activity recognition

Gan, C Lin, M Yang, Y

De Melo, G Hauptmann, AG

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Publication Type:: Conference Proceeding
Citation:: 30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 3487 - 3493
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Gan, C	en_US
dc.contributor.author	Lin, M	en_US
dc.contributor.author	Yang, Y https://orcid.org/0000-0001-5528-0546	en_US
dc.contributor.author	De Melo, G	en_US
dc.contributor.author	Hauptmann, AG	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	30th AAAI Conference on Artificial Intelligence, AAAI 2016, 2016, pp. 3487 - 3493	en_US
dc.identifier.isbn	9781577357605	en_US
dc.identifier.uri	http://hdl.handle.net/10453/104575
dc.description.abstract	© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.	en_US
dc.relation.ispartof	30th AAAI Conference on Artificial Intelligence, AAAI 2016	en_US
dc.title	Concepts not alone: Exploring pairwise relationships for zero-shot video activity recognition	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Copyright 2016, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.

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http://hdl.handle.net/10453/104575