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

Publisher:
AAAI
Publication Type:
Conference Proceeding
Citation:
Website Proceedings of 2016 AAAI Conference on Artificial Intelligence, AAAI, 2016, pp. 3487 - 3493
Issue Date:
2016
Full metadata record
Files in This Item:
Filename Description Size
12259-56312-1-PB (1).pdfPublished version728.92 kB
Adobe PDF
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.
Please use this identifier to cite or link to this item: