About: Visual Turing Test

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Computer vision research is driven by standard evaluation practices. The current systems are tested by their accuracy for tasks like object detection, segmentation and localization. Methods like the convolutional neural networks seem to be doing pretty well in these tasks, but the current systems are still not close to solving the ultimate problem of understanding images the way humans do. So motivated by the ability of humans to understand an image and even tell a story about it, Geman et al. have introduced the Visual Turing Test for computer vision systems.

Attributes	Values
rdfs:label	Visual Turing Test (en)
rdfs:comment	Computer vision research is driven by standard evaluation practices. The current systems are tested by their accuracy for tasks like object detection, segmentation and localization. Methods like the convolutional neural networks seem to be doing pretty well in these tasks, but the current systems are still not close to solving the ultimate problem of understanding images the way humans do. So motivated by the ability of humans to understand an image and even tell a story about it, Geman et al. have introduced the Visual Turing Test for computer vision systems. (en)
foaf:depiction
dcterms:subject	Human–computer interaction Computer vision Turing tests
Wikipage page ID	48589354 (xsd:integer)
Wikipage revision ID	1073420397 (xsd:integer)
Link from a Wikipage to another Wikipage	Natural language processing Johns Hopkins University Perceptron Human–computer interaction Max Planck Institute for Informatics Frank Rosenblatt Google Convolutional neural network Machine learning Computer vision Feature extraction Brown University Turing test Computer vision Ambiguity Facebook Object detection Artificial neural network Edge detection Image denoising Donald Geman Turing tests Image segmentation National Academy of Sciences Seymour Papert ImageNet Image processing Multilayer perceptron Stochastic process
Link from a Wikipage to an external page	http://www.image-net.org/ http://dspace.mit.edu/handle/1721.1/6125 https://www.eecs.berkeley.edu/Research/Projects/CS/vision/shape/h3d/ http://www.brown.edu/academics/applied-mathematics/visual-turing-test
sameAs	Visual Turing Test Visual Turing Test Visual Turing Test Visual Turing Test
dbp:wikiPageUsesTemplate	dbt:Orphan dbt:Reflist
thumbnail	wiki-commons:Special:FilePath/Sample_questions.png?width=300
has abstract	Computer vision research is driven by standard evaluation practices. The current systems are tested by their accuracy for tasks like object detection, segmentation and localization. Methods like the convolutional neural networks seem to be doing pretty well in these tasks, but the current systems are still not close to solving the ultimate problem of understanding images the way humans do. So motivated by the ability of humans to understand an image and even tell a story about it, Geman et al. have introduced the Visual Turing Test for computer vision systems. As described in, it is “an operator-assisted device that produces a stochastic sequence of binary questions from a given test image”. The query engine produces a sequence of questions that have unpredictable answers given the history of questions. The test is only about vision and does not require any natural language processing. The job of the human operator is to provide the correct answer to the question or reject it as ambiguous. The query generator produces questions such that they follow a “natural story line”, similar to what humans do when they look at a picture. (en)
prov:wasDerivedFrom	wikipedia-en:Visual_Turing_Test?oldid=1073420397&ns=0
page length (characters) of wiki page	25051 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Visual_Turing_Test
is Link from a Wikipage to another Wikipage of	List of things named after Alan Turing
is foaf:primaryTopic of	wikipedia-en:Visual_Turing_Test

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