Multimedia Question Answering

Hong, Richang; Wang, Meng; Li, Guangda; Nie, Liqiang; Zha, Zheng-Jun; Chua, Tat-Seng

doi:10.1109/mmul.2011.53

Cited by 50 publications

(13 citation statements)

References 18 publications

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“…As is known to all, in many tasks such as image classification and other multimedia content analysis [40,41], the number of training images has large influences on the final performance. From the above experiments, we can see that the performances of all the methods consistently increase with larger number of training images.…”

Section: Discussionmentioning

confidence: 99%

Image classification based on low-rank matrix recovery and Naive Bayes collaborative representation

Zhang

Hao

et al. 2015

Neurocomputing

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Section: Discussionmentioning

confidence: 99%

Image classification based on low-rank matrix recovery and Naive Bayes collaborative representation

Zhang

Hao

et al. 2015

Neurocomputing

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“…Up to now, QA research has largely focused on text, mainly targeting factual and list questions (for an overview on ontology-based Question Answering systems, see [5]). 1 However, a huge amount of increasingly multimedia contents are now available on the web on almost any topic, and it would be extremely interesting to consider them in the QA scenario, in which the best answers may be a combination of text and other media answers [4].…”

Section: Multimedia Question Answeringmentioning

confidence: 99%

Boosting QAKiS with Multimedia Answer Visualization

Cabrio

Sachidananda

Troncy

2014

Lecture Notes in Computer Science

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We present an extension of QAKiS, a system for Question Answering over DBpedia language specific chapters, that allows to complement textual answers with multimedia content from the linked data, to provide a richer and more complete answer to the user. For the demo, English, French and German DBpedia chapters are the RDF data sets to be queried using a natural language interface. Beside the textual answer, QAKiS output embeds i) pictures from Wikipedia Infoboxes, ii) OpenStreetMap, to visualize maps for questions asking about a place, and iii) YouTube, to visualize pertinent videos (e.g. movie trailers).

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“…Given the vast amount of Web content is non textual media; it is natural to extend the text based QA research to MMQA [13]. The Advantage of MMQA is many questions are better explained with the help of non textual medium.…”

Section: A From Text To Multimediamentioning

confidence: 99%

Multimedia questions and answering using web data mining

Bhaskar¹,

Singh

2014

International Conference on Information Communication and Embedded Systems (ICICES2014)

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A large amount of information returned by the web search engines like google, yahoo etc.Users become overloaded to find the correct information from the web. Question Answering (QA)approach solves these problems. Instead of returning a list of document from the current search engine, QA system provides information from a comprehensive set of well answered question with appropriate media data. QA aims to leverage in depth linguistic and media content analysis as well as domain knowledge to return precise answers to natural language questions. This paper briefly surveys the progress of multimedia question answering (MMQA) research and details its future directions. Keywords---question answering I. INTRODUCTIONThe amount of information on the web has increase year to year with content covering almost any topic. As a result, when looking for information, user becomes overloaded to find the correct information from the current search engine. Users usually have to painstakingly browse through a long list of results to look for a precise answer [1]. Therefore questions answering system solve these problems. It avoids the painstaking browsing the vast quantity of information returned by the search engines for the correct answers.

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Multimedia Question Answering

Cited by 50 publications

References 18 publications

Image classification based on low-rank matrix recovery and Naive Bayes collaborative representation

Image classification based on low-rank matrix recovery and Naive Bayes collaborative representation

Boosting QAKiS with Multimedia Answer Visualization

Multimedia questions and answering using web data mining

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