NDLI: Zero-Shot Event Detection Using Multi-modal Fusion of Weakly Supervised Concepts

Content Provider	IEEE Xplore Digital Library
Author	Shuang Wu Bondugula, S. Luisier, F. Xiaodan Zhuang Natarajan, P.
Copyright Year	2014
Description	Author affiliation: Speech, Language & Multimedia, Raytheon BBN Technol., Cambridge, MA, USA (Shuang Wu; Luisier, F.; Xiaodan Zhuang; Natarajan, P.) \|\| Dept. of Comput. Sci., Univ. of Maryland, College Park, MD, USA (Bondugula, S.)
Abstract	Current state-of-the-art systems for visual content analysis require large training sets for each class of interest, and performance degrades rapidly with fewer examples. In this paper, we present a general framework for the zeroshot learning problem of performing high-level event detection with no training exemplars, using only textual descriptions. This task goes beyond the traditional zero-shot framework of adapting a given set of classes with training data to unseen classes. We leverage video and image collections with free-form text descriptions from widely available web sources to learn a large bank of concepts, in addition to using several off-the-shelf concept detectors, speech, and video text for representing videos. We utilize natural language processing technologies to generate event description features. The extracted features are then projected to a common high-dimensional space using text expansion, and similarity is computed in this space. We present extensive experimental results on the large TRECVID MED [26] corpus to demonstrate our approach. Our results show that the proposed concept detection methods significantly outperform current attribute classifiers such as Classemes [34], ObjectBank [21], and SUN attributes[28] . Further, we find that fusion, both within as well as between modalities, is crucial for optimal performance.
Starting Page	2665
Ending Page	2672
File Size	551204
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.341
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Vectors Detectors Training Speech Visualization Support vector machines Multimodal Fusion Zero-shot Learning Video Event Detection Concept Detection
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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