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Keywords {🔍}

google, scholar, object, detection, cvpr, computer, eccv, part, recognition, chapter, research, paper, error, detectors, science, zisserman, visual, privacy, cookies, analysis, information, publish, vision, conference, hoiem, similar, objects, perona, institute, springer, heidelberg, content, search, classes, vedaldi, detector, felzenszwalb, iccv, ramanan, models, schmid, article, data, journal, chodpathumwan, dai, localization, multiple, study, download,

Topics {✒️}

org/challenges/voc/voc2007/workshop/index technical report cmu-ri-tr-01-02 hog-lbp human detector /~pff/latent-release4/ park technical report cns-tr-2010-001 multi-view probabilistic model fine-grained image categorization flexible object models diagnosing error 3d object classes localization error privacy choices/manage cookies perform similar analysis context based recognition semantically similar objects deformable part model layered object detection object detection applied learning algorithm improves main content log future researchers multi-class segmentation visual scene understanding edgelet part detectors caltech-ucsd birds 200 recognition research achieve large gains partially occluded humans structured output regression interest point detectors plos computational biology 4 conditions privacy policy paper cite european economic area accepting optional cookies object detectors similar objects nsf awards iis-1053768 discriminatively trained detailed analysis essential european conference partial occlusion handling object detection computer science technical report 7694 conference series objects classification approaches journal finder publish machine vision object characteristics

Questions {❓}

• Why is real-world visual object recognition hard?

Schema {🗺️}

ScholarlyArticle:
      headline:Diagnosing Error in Object Detectors
      pageEnd:353
      pageStart:340
      image:https://media.springernature.com/w153/springer-static/cover/book/978-3-642-33712-3.jpg
      genre:
         Computer Science
         Computer Science (R0)
      isPartOf:
         name:Computer Vision – ECCV 2012
         isbn:
            978-3-642-33712-3
            978-3-642-33711-6
         type:Book
      publisher:
         name:Springer Berlin Heidelberg
         logo:
            url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
            type:ImageObject
         type:Organization
      author:
            name:Derek Hoiem
            affiliation:
                  name:University of Illinois at Urbana-Champaign
                  address:
                     name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Yodsawalai Chodpathumwan
            affiliation:
                  name:University of Illinois at Urbana-Champaign
                  address:
                     name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
                     type:PostalAddress
                  type:Organization
            type:Person
            name:Qieyun Dai
            affiliation:
                  name:University of Illinois at Urbana-Champaign
                  address:
                     name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
                     type:PostalAddress
                  type:Organization
            type:Person
      keywords:Localization Error, Object Detector, Average Precision, Object Category, Similar Object
      description:This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives. In particular, we examine effects of occlusion, size, aspect ratio, visibility of parts, viewpoint, localization error, and confusion with semantically similar objects, other labeled objects, and background. We analyze two classes of detectors: the Vedaldi et al. multiple kernel learning detector and different versions of the Felzenszwalb et al. detector. Our study shows that sensitivity to size, localization error, and confusion with similar objects are the most impactful forms of error. Our analysis also reveals that many different kinds of improvement are necessary to achieve large gains, making more detailed analysis essential for the progress of recognition research. By making our software and annotations available, we make it effortless for future researchers to perform similar analysis.
      datePublished:2012
      isAccessibleForFree:1
      context:https://schema.org
Book:
      name:Computer Vision – ECCV 2012
      isbn:
         978-3-642-33712-3
         978-3-642-33711-6
Organization:
      name:Springer Berlin Heidelberg
      logo:
         url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
         type:ImageObject
      name:University of Illinois at Urbana-Champaign
      address:
         name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
         type:PostalAddress
      name:University of Illinois at Urbana-Champaign
      address:
         name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
         type:PostalAddress
      name:University of Illinois at Urbana-Champaign
      address:
         name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
         type:PostalAddress
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      url:https://www.springernature.com/app-sn/public/images/logo-springernature.png
Person:
      name:Derek Hoiem
      affiliation:
            name:University of Illinois at Urbana-Champaign
            address:
               name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
               type:PostalAddress
            type:Organization
      name:Yodsawalai Chodpathumwan
      affiliation:
            name:University of Illinois at Urbana-Champaign
            address:
               name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
               type:PostalAddress
            type:Organization
      name:Qieyun Dai
      affiliation:
            name:University of Illinois at Urbana-Champaign
            address:
               name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
      name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA
      name:Department of Computer Science, University of Illinois at Urbana-Champaign, USA

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