Efficiently Scaling up Crowdsourced Video Annotation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-01

AUTHORS

Carl Vondrick, Donald Patterson, Deva Ramanan

ABSTRACT

We present an extensive three year study on economically annotating video with crowdsourced marketplaces. Our public framework has annotated thousands of real world videos, including massive data sets unprecedented for their size, complexity, and cost. To accomplish this, we designed a state-of-the-art video annotation user interface and demonstrate that, despite common intuition, many contemporary interfaces are sub-optimal. We present several user studies that evaluate different aspects of our system and demonstrate that minimizing the cognitive load of the user is crucial when designing an annotation platform. We then deploy this interface on Amazon Mechanical Turk and discover expert and talented workers who are capable of annotating difficult videos with dense and closely cropped labels. We argue that video annotation requires specialized skill; most workers are poor annotators, mandating robust quality control protocols. We show that traditional crowdsourced micro-tasks are not suitable for video annotation and instead demonstrate that deploying time-consuming macro-tasks on MTurk is effective. Finally, we show that by extracting pixel-based features from manually labeled key frames, we are able to leverage more sophisticated interpolation strategies to maximize performance given a fixed budget. We validate the power of our framework on difficult, real-world data sets and we demonstrate an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling. We further introduce a novel, cost-based evaluation criteria that compares vision algorithms by the budget required to achieve an acceptable performance. We hope our findings will spur innovation in the creation of massive labeled video data sets and enable novel data-driven computer vision applications. More... »

PAGES

184-204

References to SciGraph publications

  • 2010. Efficiently Scaling Up Video Annotation with Crowdsourced Marketplaces in COMPUTER VISION – ECCV 2010
  • 2001-05. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2008-05. LabelMe: A Database and Web-Based Tool for Image Annotation in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2010-06. The Pascal Visual Object Classes (VOC) Challenge in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11263-012-0564-1

    DOI

    http://dx.doi.org/10.1007/s11263-012-0564-1

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1024892064


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Massachusetts Institute of Technology", 
              "id": "https://www.grid.ac/institutes/grid.116068.8", 
              "name": [
                "Department of Computer Science, UC Irvine, Irvine, USA", 
                "Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Vondrick", 
            "givenName": "Carl", 
            "id": "sg:person.011554751423.38", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011554751423.38"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of California, Irvine", 
              "id": "https://www.grid.ac/institutes/grid.266093.8", 
              "name": [
                "Department of Informatics, UC Irvine, Irvine, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Patterson", 
            "givenName": "Donald", 
            "id": "sg:person.016065540770.63", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016065540770.63"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of California, Irvine", 
              "id": "https://www.grid.ac/institutes/grid.266093.8", 
              "name": [
                "Department of Computer Science, UC Irvine, Irvine, USA"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ramanan", 
            "givenName": "Deva", 
            "id": "sg:person.010041565404.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010041565404.26"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1145/1054972.1055017", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000599469"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1177352.1177355", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003634065"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-009-0275-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014796149", 
              "https://doi.org/10.1007/s11263-009-0275-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-009-0275-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014796149", 
              "https://doi.org/10.1007/s11263-009-0275-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chb.2005.12.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018505813"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1011139631724", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019562355", 
              "https://doi.org/10.1023/a:1011139631724"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/985692.985733", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021652857"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s11263-007-0090-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027534025", 
              "https://doi.org/10.1007/s11263-007-0090-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-15561-1_44", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030274604", 
              "https://doi.org/10.1007/978-3-642-15561-1_44"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1124772.1124782", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032051655"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1015706.1015764", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039959794"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1073/pnas.42.10.767", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040144814"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1753846.1753873", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045685410"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1178677.1178722", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045741294"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/jproc.2010.2050290", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061297260"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tpami.2008.128", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061743490"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1137/0105003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062837605"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2007.4409012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093419161"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvprw.2008.4562953", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093557144"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2010.5539970", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093603006"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2009.5459289", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093830166"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2008.4587845", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093874696"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2005.177", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093997066"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2009.5206705", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094051431"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/siu.2012.6204823", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094719041"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2011.5995403", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094756084"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2008.4587756", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094776319"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2010.5540055", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095170315"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2011.5995586", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095259219"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvprw.2010.5543183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095322885"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2006.158", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095559554"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2012.6248010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095631759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cvpr.2009.5206848", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095689025"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/iccv.2009.5459250", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095774085"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.5244/c.25.109", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1099341327"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2013-01", 
        "datePublishedReg": "2013-01-01", 
        "description": "We present an extensive three year study on economically annotating video with crowdsourced marketplaces. Our public framework has annotated thousands of real world videos, including massive data sets unprecedented for their size, complexity, and cost. To accomplish this, we designed a state-of-the-art video annotation user interface and demonstrate that, despite common intuition, many contemporary interfaces are sub-optimal. We present several user studies that evaluate different aspects of our system and demonstrate that minimizing the cognitive load of the user is crucial when designing an annotation platform. We then deploy this interface on Amazon Mechanical Turk and discover expert and talented workers who are capable of annotating difficult videos with dense and closely cropped labels. We argue that video annotation requires specialized skill; most workers are poor annotators, mandating robust quality control protocols. We show that traditional crowdsourced micro-tasks are not suitable for video annotation and instead demonstrate that deploying time-consuming macro-tasks on MTurk is effective. Finally, we show that by extracting pixel-based features from manually labeled key frames, we are able to leverage more sophisticated interpolation strategies to maximize performance given a fixed budget. We validate the power of our framework on difficult, real-world data sets and we demonstrate an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling. We further introduce a novel, cost-based evaluation criteria that compares vision algorithms by the budget required to achieve an acceptable performance. We hope our findings will spur innovation in the creation of massive labeled video data sets and enable novel data-driven computer vision applications.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11263-012-0564-1", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isFundedItemOf": [
          {
            "id": "sg:grant.4402000", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3089609", 
            "type": "MonetaryGrant"
          }, 
          {
            "id": "sg:grant.3109391", 
            "type": "MonetaryGrant"
          }
        ], 
        "isPartOf": [
          {
            "id": "sg:journal.1032807", 
            "issn": [
              "0920-5691", 
              "1573-1405"
            ], 
            "name": "International Journal of Computer Vision", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "101"
          }
        ], 
        "name": "Efficiently Scaling up Crowdsourced Video Annotation", 
        "pagination": "184-204", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "7696591311184d88b3e1029858ae4f9e190cfc2e33520b7e36b2281fdb09cc82"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11263-012-0564-1"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1024892064"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11263-012-0564-1", 
          "https://app.dimensions.ai/details/publication/pub.1024892064"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T22:34", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8690_00000522.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007%2Fs11263-012-0564-1"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11263-012-0564-1'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11263-012-0564-1'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11263-012-0564-1'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11263-012-0564-1'


     

    This table displays all metadata directly associated to this object as RDF triples.

    192 TRIPLES      21 PREDICATES      61 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11263-012-0564-1 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Ndb48dd59ccd7498c84f1fec3630e0495
    4 schema:citation sg:pub.10.1007/978-3-642-15561-1_44
    5 sg:pub.10.1007/s11263-007-0090-8
    6 sg:pub.10.1007/s11263-009-0275-4
    7 sg:pub.10.1023/a:1011139631724
    8 https://doi.org/10.1016/j.chb.2005.12.009
    9 https://doi.org/10.1073/pnas.42.10.767
    10 https://doi.org/10.1109/cvpr.2005.177
    11 https://doi.org/10.1109/cvpr.2006.158
    12 https://doi.org/10.1109/cvpr.2008.4587756
    13 https://doi.org/10.1109/cvpr.2008.4587845
    14 https://doi.org/10.1109/cvpr.2009.5206705
    15 https://doi.org/10.1109/cvpr.2009.5206848
    16 https://doi.org/10.1109/cvpr.2010.5539970
    17 https://doi.org/10.1109/cvpr.2010.5540055
    18 https://doi.org/10.1109/cvpr.2011.5995403
    19 https://doi.org/10.1109/cvpr.2011.5995586
    20 https://doi.org/10.1109/cvpr.2012.6248010
    21 https://doi.org/10.1109/cvprw.2008.4562953
    22 https://doi.org/10.1109/cvprw.2010.5543183
    23 https://doi.org/10.1109/iccv.2007.4409012
    24 https://doi.org/10.1109/iccv.2009.5459250
    25 https://doi.org/10.1109/iccv.2009.5459289
    26 https://doi.org/10.1109/jproc.2010.2050290
    27 https://doi.org/10.1109/siu.2012.6204823
    28 https://doi.org/10.1109/tpami.2008.128
    29 https://doi.org/10.1137/0105003
    30 https://doi.org/10.1145/1015706.1015764
    31 https://doi.org/10.1145/1054972.1055017
    32 https://doi.org/10.1145/1124772.1124782
    33 https://doi.org/10.1145/1177352.1177355
    34 https://doi.org/10.1145/1178677.1178722
    35 https://doi.org/10.1145/1753846.1753873
    36 https://doi.org/10.1145/985692.985733
    37 https://doi.org/10.5244/c.25.109
    38 schema:datePublished 2013-01
    39 schema:datePublishedReg 2013-01-01
    40 schema:description We present an extensive three year study on economically annotating video with crowdsourced marketplaces. Our public framework has annotated thousands of real world videos, including massive data sets unprecedented for their size, complexity, and cost. To accomplish this, we designed a state-of-the-art video annotation user interface and demonstrate that, despite common intuition, many contemporary interfaces are sub-optimal. We present several user studies that evaluate different aspects of our system and demonstrate that minimizing the cognitive load of the user is crucial when designing an annotation platform. We then deploy this interface on Amazon Mechanical Turk and discover expert and talented workers who are capable of annotating difficult videos with dense and closely cropped labels. We argue that video annotation requires specialized skill; most workers are poor annotators, mandating robust quality control protocols. We show that traditional crowdsourced micro-tasks are not suitable for video annotation and instead demonstrate that deploying time-consuming macro-tasks on MTurk is effective. Finally, we show that by extracting pixel-based features from manually labeled key frames, we are able to leverage more sophisticated interpolation strategies to maximize performance given a fixed budget. We validate the power of our framework on difficult, real-world data sets and we demonstrate an inherent trade-off between the mix of human and cloud computing used vs. the accuracy and cost of the labeling. We further introduce a novel, cost-based evaluation criteria that compares vision algorithms by the budget required to achieve an acceptable performance. We hope our findings will spur innovation in the creation of massive labeled video data sets and enable novel data-driven computer vision applications.
    41 schema:genre research_article
    42 schema:inLanguage en
    43 schema:isAccessibleForFree false
    44 schema:isPartOf N0af0e4cb161a41468ab6fa0242753113
    45 Nfbc3ec6368794d3c88cc5644d6543f25
    46 sg:journal.1032807
    47 schema:name Efficiently Scaling up Crowdsourced Video Annotation
    48 schema:pagination 184-204
    49 schema:productId N4be36661fcb843aa8d41962b4257f9c6
    50 N8b5da4be02ae483cb504342282178f62
    51 Na7f9e1d1a4fd4332ab432ad3c96863e8
    52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024892064
    53 https://doi.org/10.1007/s11263-012-0564-1
    54 schema:sdDatePublished 2019-04-10T22:34
    55 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    56 schema:sdPublisher N0d4a8e831c844f8fb6a8ac196c834411
    57 schema:url http://link.springer.com/10.1007%2Fs11263-012-0564-1
    58 sgo:license sg:explorer/license/
    59 sgo:sdDataset articles
    60 rdf:type schema:ScholarlyArticle
    61 N0af0e4cb161a41468ab6fa0242753113 schema:volumeNumber 101
    62 rdf:type schema:PublicationVolume
    63 N0d4a8e831c844f8fb6a8ac196c834411 schema:name Springer Nature - SN SciGraph project
    64 rdf:type schema:Organization
    65 N1f55a29690f44410be9ecb62309ff760 rdf:first sg:person.010041565404.26
    66 rdf:rest rdf:nil
    67 N4be36661fcb843aa8d41962b4257f9c6 schema:name dimensions_id
    68 schema:value pub.1024892064
    69 rdf:type schema:PropertyValue
    70 N8b5da4be02ae483cb504342282178f62 schema:name readcube_id
    71 schema:value 7696591311184d88b3e1029858ae4f9e190cfc2e33520b7e36b2281fdb09cc82
    72 rdf:type schema:PropertyValue
    73 Na7f9e1d1a4fd4332ab432ad3c96863e8 schema:name doi
    74 schema:value 10.1007/s11263-012-0564-1
    75 rdf:type schema:PropertyValue
    76 Ndb48dd59ccd7498c84f1fec3630e0495 rdf:first sg:person.011554751423.38
    77 rdf:rest Ne26c159791054d58a1eb380216efab22
    78 Ne26c159791054d58a1eb380216efab22 rdf:first sg:person.016065540770.63
    79 rdf:rest N1f55a29690f44410be9ecb62309ff760
    80 Nfbc3ec6368794d3c88cc5644d6543f25 schema:issueNumber 1
    81 rdf:type schema:PublicationIssue
    82 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    83 schema:name Information and Computing Sciences
    84 rdf:type schema:DefinedTerm
    85 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    86 schema:name Artificial Intelligence and Image Processing
    87 rdf:type schema:DefinedTerm
    88 sg:grant.3089609 http://pending.schema.org/fundedItem sg:pub.10.1007/s11263-012-0564-1
    89 rdf:type schema:MonetaryGrant
    90 sg:grant.3109391 http://pending.schema.org/fundedItem sg:pub.10.1007/s11263-012-0564-1
    91 rdf:type schema:MonetaryGrant
    92 sg:grant.4402000 http://pending.schema.org/fundedItem sg:pub.10.1007/s11263-012-0564-1
    93 rdf:type schema:MonetaryGrant
    94 sg:journal.1032807 schema:issn 0920-5691
    95 1573-1405
    96 schema:name International Journal of Computer Vision
    97 rdf:type schema:Periodical
    98 sg:person.010041565404.26 schema:affiliation https://www.grid.ac/institutes/grid.266093.8
    99 schema:familyName Ramanan
    100 schema:givenName Deva
    101 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010041565404.26
    102 rdf:type schema:Person
    103 sg:person.011554751423.38 schema:affiliation https://www.grid.ac/institutes/grid.116068.8
    104 schema:familyName Vondrick
    105 schema:givenName Carl
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011554751423.38
    107 rdf:type schema:Person
    108 sg:person.016065540770.63 schema:affiliation https://www.grid.ac/institutes/grid.266093.8
    109 schema:familyName Patterson
    110 schema:givenName Donald
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016065540770.63
    112 rdf:type schema:Person
    113 sg:pub.10.1007/978-3-642-15561-1_44 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030274604
    114 https://doi.org/10.1007/978-3-642-15561-1_44
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/s11263-007-0090-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027534025
    117 https://doi.org/10.1007/s11263-007-0090-8
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1007/s11263-009-0275-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014796149
    120 https://doi.org/10.1007/s11263-009-0275-4
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1023/a:1011139631724 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019562355
    123 https://doi.org/10.1023/a:1011139631724
    124 rdf:type schema:CreativeWork
    125 https://doi.org/10.1016/j.chb.2005.12.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018505813
    126 rdf:type schema:CreativeWork
    127 https://doi.org/10.1073/pnas.42.10.767 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040144814
    128 rdf:type schema:CreativeWork
    129 https://doi.org/10.1109/cvpr.2005.177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093997066
    130 rdf:type schema:CreativeWork
    131 https://doi.org/10.1109/cvpr.2006.158 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095559554
    132 rdf:type schema:CreativeWork
    133 https://doi.org/10.1109/cvpr.2008.4587756 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094776319
    134 rdf:type schema:CreativeWork
    135 https://doi.org/10.1109/cvpr.2008.4587845 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093874696
    136 rdf:type schema:CreativeWork
    137 https://doi.org/10.1109/cvpr.2009.5206705 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094051431
    138 rdf:type schema:CreativeWork
    139 https://doi.org/10.1109/cvpr.2009.5206848 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095689025
    140 rdf:type schema:CreativeWork
    141 https://doi.org/10.1109/cvpr.2010.5539970 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093603006
    142 rdf:type schema:CreativeWork
    143 https://doi.org/10.1109/cvpr.2010.5540055 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095170315
    144 rdf:type schema:CreativeWork
    145 https://doi.org/10.1109/cvpr.2011.5995403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094756084
    146 rdf:type schema:CreativeWork
    147 https://doi.org/10.1109/cvpr.2011.5995586 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095259219
    148 rdf:type schema:CreativeWork
    149 https://doi.org/10.1109/cvpr.2012.6248010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095631759
    150 rdf:type schema:CreativeWork
    151 https://doi.org/10.1109/cvprw.2008.4562953 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093557144
    152 rdf:type schema:CreativeWork
    153 https://doi.org/10.1109/cvprw.2010.5543183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095322885
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1109/iccv.2007.4409012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093419161
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1109/iccv.2009.5459250 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095774085
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1109/iccv.2009.5459289 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093830166
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1109/jproc.2010.2050290 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061297260
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1109/siu.2012.6204823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094719041
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1109/tpami.2008.128 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061743490
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1137/0105003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062837605
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1145/1015706.1015764 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039959794
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1145/1054972.1055017 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000599469
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1145/1124772.1124782 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032051655
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1145/1177352.1177355 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003634065
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1145/1178677.1178722 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045741294
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1145/1753846.1753873 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045685410
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1145/985692.985733 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021652857
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.5244/c.25.109 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099341327
    184 rdf:type schema:CreativeWork
    185 https://www.grid.ac/institutes/grid.116068.8 schema:alternateName Massachusetts Institute of Technology
    186 schema:name Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
    187 Department of Computer Science, UC Irvine, Irvine, USA
    188 rdf:type schema:Organization
    189 https://www.grid.ac/institutes/grid.266093.8 schema:alternateName University of California, Irvine
    190 schema:name Department of Computer Science, UC Irvine, Irvine, USA
    191 Department of Informatics, UC Irvine, Irvine, USA
    192 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...