Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle th...

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Vydané v:	International journal of computer vision Ročník 123; číslo 1; s. 32 - 73
Hlavní autori:	Krishna, Ranjay, Zhu, Yuke, Groth, Oliver, Johnson, Justin, Hata, Kenji, Kravitz, Joshua, Chen, Stephanie, Kalantidis, Yannis, Li, Li-Jia, Shamma, David A., Bernstein, Michael S., Fei-Fei, Li
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.05.2017 Springer Springer Nature B.V
Predmet:	Analysis Annotations Artificial Intelligence Carriages Cognition & reasoning Cognitive tasks Computer Imaging Computer Science Computer simulation Computer vision Computers Crowdsourcing Datasets Genomes Genomics Graph representations Image Processing and Computer Vision Image processing systems Image retrieval Language Mathematical models Natural language processing Pattern Recognition Pattern Recognition and Graphics Questioning Studies Tasks Vision Vision systems United States > US Crowdsourcing Computer vision Relationships Scene graph Language Dataset Objects Attributes Question answering Image Knowledge
ISSN:	0920-5691, 1573-1405
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Abstract	Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.
AbstractList	Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of [Formula omitted] objects, [Formula omitted] attributes, and [Formula omitted] pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs. (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked "What vehicle is the person riding?", computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of ... objects, ... attributes, and ... pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs. Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in an image. When asked “What vehicle is the person riding?”, computers will need to identify the objects in an image as well as the relationships riding(man, carriage) and pulling(horse, carriage) to answer correctly that “the person is riding a horse-drawn carriage.” In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of 35 objects, 26 attributes, and 21 pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.
Audience	Academic
Author	Hata, Kenji Li, Li-Jia Zhu, Yuke Kravitz, Joshua Shamma, David A. Johnson, Justin Fei-Fei, Li Kalantidis, Yannis Bernstein, Michael S. Krishna, Ranjay Groth, Oliver Chen, Stephanie
Author_xml	– sequence: 1 givenname: Ranjay orcidid: 0000-0001-8784-2531 surname: Krishna fullname: Krishna, Ranjay email: ranjaykrishna@cs.stanford.edu organization: Stanford University – sequence: 2 givenname: Yuke surname: Zhu fullname: Zhu, Yuke organization: Stanford University – sequence: 3 givenname: Oliver surname: Groth fullname: Groth, Oliver organization: Dresden University of Technology – sequence: 4 givenname: Justin surname: Johnson fullname: Johnson, Justin organization: Stanford University – sequence: 5 givenname: Kenji surname: Hata fullname: Hata, Kenji organization: Stanford University – sequence: 6 givenname: Joshua surname: Kravitz fullname: Kravitz, Joshua organization: Stanford University – sequence: 7 givenname: Stephanie surname: Chen fullname: Chen, Stephanie organization: Stanford University – sequence: 8 givenname: Yannis surname: Kalantidis fullname: Kalantidis, Yannis organization: Yahoo Inc – sequence: 9 givenname: Li-Jia surname: Li fullname: Li, Li-Jia organization: Snapchat Inc – sequence: 10 givenname: David A. surname: Shamma fullname: Shamma, David A. organization: Centrum Wiskunde & Informatica (CWI) – sequence: 11 givenname: Michael S. surname: Bernstein fullname: Bernstein, Michael S. organization: Stanford University – sequence: 12 givenname: Li surname: Fei-Fei fullname: Fei-Fei, Li organization: Stanford University
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