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

Content Provider	Springer Nature Link
Author	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
Copyright Year	2017
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.
Starting Page	32
Ending Page	73
Page Count	42
File Format	PDF
ISSN	09205691
Journal	International Journal of Computer Vision
Volume Number	123
Issue Number	1
e-ISSN	15731405
Language	English
Publisher	Springer US
Publisher Date	2017-02-06
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computer vision Dataset Image Scene graph Question answering Objects Attributes Relationships Knowledge Language Crowdsourcing Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Image Processing and Computer Vision Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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

VQA: Visual Question Answering : www.visualqa.org

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

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

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