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The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale

International journal of computer vision, 2020-07, Vol.128 (7), p.1956-1981 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-020-01316-z

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  • Title:
    The Open Images Dataset V4: Unified Image Classification, Object Detection, and Visual Relationship Detection at Scale
  • Author: Kuznetsova, Alina ; Rom, Hassan ; Alldrin, Neil ; Uijlings, Jasper ; Krasin, Ivan ; Pont-Tuset, Jordi ; Kamali, Shahab ; Popov, Stefan ; Malloci, Matteo ; Kolesnikov, Alexander ; Duerig, Tom ; Ferrari, Vittorio
  • Subjects: Artificial Intelligence ; Computer Imaging ; Computer Science ; Image Processing and Computer Vision ; Pattern Recognition ; Pattern Recognition and Graphics ; Vision
  • Is Part Of: International journal of computer vision, 2020-07, Vol.128 (7), p.1956-1981
  • Description: We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection. The images have a Creative Commons Attribution license that allows to share and adapt the material, and they have been collected from Flickr without a predefined list of class names or tags, leading to natural class statistics and avoiding an initial design bias. Open Images V4 offers large scale across several dimensions: 30.1M image-level labels for 19.8k concepts, 15.4M bounding boxes for 600 object classes, and 375k visual relationship annotations involving 57 classes. For object detection in particular, we provide 15 × more bounding boxes than the next largest datasets (15.4M boxes on 1.9M images). The images often show complex scenes with several objects (8 annotated objects per image on average). We annotated visual relationships between them, which support visual relationship detection, an emerging task that requires structured reasoning. We provide in-depth comprehensive statistics about the dataset, we validate the quality of the annotations, we study how the performance of several modern models evolves with increasing amounts of training data, and we demonstrate two applications made possible by having unified annotations of multiple types coexisting in the same images. We hope that the scale, quality, and variety of Open Images V4 will foster further research and innovation even beyond the areas of image classification, object detection, and visual relationship detection.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0920-5691
    EISSN: 1573-1405
    DOI: 10.1007/s11263-020-01316-z
  • Source: AUTh Library subscriptions: ProQuest Central
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