We are analyzing https://link.springer.com/chapter/10.1007/978-3-319-10602-1_48.

Title:
Microsoft COCO: Common Objects in Context | SpringerLink
Description:
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes...
Website Age:
28 years and 1 months (reg. 1997-05-29).

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Keywords {🔍}

google, scholar, object, cvpr, detection, segmentation, image, objects, perona, computer, eccv, chapter, database, article, berg, context, ramanan, images, learning, springer, data, part, dataset, recognition, pami, privacy, cookies, information, publish, search, vision, conference, paper, common, hays, science, scene, ijcv, evaluation, models, technical, usa, content, research, microsoft, lin, maire, belongie, dollár, instance,

Topics {✒️}

large-scale scene recognition web-scale image search multi-class object recognition scalable multi-label annotation deformable parts model tsung-yi lin technical report cns-tr-201 layered object models scene understanding privacy choices/manage cookies contextual object detection accurate object detection semantic object classes verify object hypotheses category detection recognizing scene attributes computer science department czech technical university main content log precise object localization web-based tool microsoft coco large data set caltech-ucsd birds 200 collecting image annotations person pose estimation class attribute transfer image understanding hierarchical image segmentation complex everyday scenes detailed statistical analysis learning algorithm improves rich feature hierarchies richly annotated catalog learning multiple layers jointly modeling texture electronic lexical database paper lin conditions privacy policy european economic area entry-level categories paper cite finding iconic images segmentation detection results object detection accepting optional cookies computer science object detectors learning spatial context localize detected objects

Questions {❓}

Detecting avocados to zucchinis: what have we done, and where are we going?
Do we need more training data or better models for object detection?

Schema {🗺️}

ScholarlyArticle:
      headline:Microsoft COCO: Common Objects in Context
      pageEnd:755
      pageStart:740
      image:https://media.springernature.com/w153/springer-static/cover/book/978-3-319-10602-1.jpg
      genre:
         Computer Science
         Computer Science (R0)
      isPartOf:
         name:Computer Vision – ECCV 2014
         isbn:
            978-3-319-10602-1
            978-3-319-10601-4
         type:Book
      publisher:
         name:Springer International Publishing
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Tsung-Yi Lin
            affiliation:
                  name:Cornell
                  address:
                     name:Cornell, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Michael Maire
            affiliation:
                  name:Caltech
                  address:
                     name:Caltech, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Serge Belongie
            affiliation:
                  name:Cornell
                  address:
                     name:Cornell, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:James Hays
            affiliation:
                  name:Brown
                  address:
                     name:Brown, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Pietro Perona
            affiliation:
                  name:Caltech
                  address:
                     name:Caltech, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Deva Ramanan
            affiliation:
                  name:UC Irvine
                  address:
                     name:UC Irvine, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Piotr Dollár
            affiliation:
                  name:Microsoft Research
                  address:
                     name:Microsoft Research, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:C. Lawrence Zitnick
            affiliation:
                  name:Microsoft Research
                  address:
                     name:Microsoft Research, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:Object Detection, Common Object, Object Category, Object Instance, Scene Understanding
      description:We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4 year old. With a total of 2.5 million labeled instances in 328k images, the creation of our dataset drew upon extensive crowd worker involvement via novel user interfaces for category detection, instance spotting and instance segmentation. We present a detailed statistical analysis of the dataset in comparison to PASCAL, ImageNet, and SUN. Finally, we provide baseline performance analysis for bounding box and segmentation detection results using a Deformable Parts Model.
      datePublished:2014
      isAccessibleForFree:1
      context:https://schema.org
Book:
      name:Computer Vision – ECCV 2014
      isbn:
         978-3-319-10602-1
         978-3-319-10601-4
Organization:
      name:Springer International Publishing
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:Cornell
      address:
         name:Cornell, USA
         type:PostalAddress
      name:Caltech
      address:
         name:Caltech, USA
         type:PostalAddress
      name:Cornell
      address:
         name:Cornell, USA
         type:PostalAddress
      name:Brown
      address:
         name:Brown, USA
         type:PostalAddress
      name:Caltech
      address:
         name:Caltech, USA
         type:PostalAddress
      name:UC Irvine
      address:
         name:UC Irvine, USA
         type:PostalAddress
      name:Microsoft Research
      address:
         name:Microsoft Research, USA
         type:PostalAddress
      name:Microsoft Research
      address:
         name:Microsoft Research, USA
         type:PostalAddress
ImageObject:
      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Tsung-Yi Lin
      affiliation:
            name:Cornell
            address:
               name:Cornell, USA
               type:PostalAddress
            type:Organization
      name:Michael Maire
      affiliation:
            name:Caltech
            address:
               name:Caltech, USA
               type:PostalAddress
            type:Organization
      name:Serge Belongie
      affiliation:
            name:Cornell
            address:
               name:Cornell, USA
               type:PostalAddress
            type:Organization
      name:James Hays
      affiliation:
            name:Brown
            address:
               name:Brown, USA
               type:PostalAddress
            type:Organization
      name:Pietro Perona
      affiliation:
            name:Caltech
            address:
               name:Caltech, USA
               type:PostalAddress
            type:Organization
      name:Deva Ramanan
      affiliation:
            name:UC Irvine
            address:
               name:UC Irvine, USA
               type:PostalAddress
            type:Organization
      name:Piotr Dollár
      affiliation:
            name:Microsoft Research
            address:
               name:Microsoft Research, USA
               type:PostalAddress
            type:Organization
      name:C. Lawrence Zitnick
      affiliation:
            name:Microsoft Research
            address:
               name:Microsoft Research, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Cornell, USA
      name:Caltech, USA
      name:Cornell, USA
      name:Brown, USA
      name:Caltech, USA
      name:UC Irvine, USA
      name:Microsoft Research, USA
      name:Microsoft Research, USA

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