Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2014

AUTHORS

Kaiming He , Xiangyu Zhang , Shaoqing Ren , Jian Sun

ABSTRACT

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. 224×224) input image. This requirement is “artificial” and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101. The power of SPP-net is more significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method computes convolutional features 30-170× faster than the recent leading method R-CNN (and 24-64× faster overall), while achieving better or comparable accuracy on Pascal VOC 2007. More... »

PAGES

346-361

References to SciGraph publications

Book

TITLE

Computer Vision – ECCV 2014

ISBN

978-3-319-10577-2
978-3-319-10578-9

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-10578-9_23

DOI

http://dx.doi.org/10.1007/978-3-319-10578-9_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030406568


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": {
          "name": [
            "Microsoft Research, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "He", 
        "givenName": "Kaiming", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Xi'an Jiaotong University", 
          "id": "https://www.grid.ac/institutes/grid.43169.39", 
          "name": [
            "Xi\u2019an Jiaotong University, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Xiangyu", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "University of Science and Technology, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ren", 
        "givenName": "Shaoqing", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "name": [
            "Microsoft Research, China"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Jian", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/j.cviu.2005.09.012", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004784969"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1162/neco.1989.1.4.541", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1008345178"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-319-10584-0_26", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032984348", 
          "https://doi.org/10.1007/978-3-319-10584-0_26"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-15561-1_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045344996", 
          "https://doi.org/10.1007/978-3-642-15561-1_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-642-15561-1_11", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045344996", 
          "https://doi.org/10.1007/978-3-642-15561-1_11"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-88690-7_52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048787563", 
          "https://doi.org/10.1007/978-3-540-88690-7_52"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-3-540-88690-7_52", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048787563", 
          "https://doi.org/10.1007/978-3-540-88690-7_52"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2014.220", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1052782426"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2014.212", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093810850"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2013.10", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1093883984"
        ], 
        "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.2014.222", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094012327"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2006.68", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094512911"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2014.81", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094727707"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2003.1238663", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1094978467"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2009.5206757", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095180230"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/cvpr.2010.5540018", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095506116"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1109/iccv.2005.239", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1095611654"
        ], 
        "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/10.5244/c.25.76", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099341617"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g.\u00a0224\u00d7224) input image. This requirement is \u201cartificial\u201d and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, \u201cspatial pyramid pooling\u201d, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101. The power of SPP-net is more significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method computes convolutional features 30-170\u00d7 faster than the recent leading method R-CNN (and 24-64\u00d7 faster overall), while achieving better or comparable accuracy on Pascal VOC 2007.", 
    "editor": [
      {
        "familyName": "Fleet", 
        "givenName": "David", 
        "type": "Person"
      }, 
      {
        "familyName": "Pajdla", 
        "givenName": "Tomas", 
        "type": "Person"
      }, 
      {
        "familyName": "Schiele", 
        "givenName": "Bernt", 
        "type": "Person"
      }, 
      {
        "familyName": "Tuytelaars", 
        "givenName": "Tinne", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-10578-9_23", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-319-10577-2", 
        "978-3-319-10578-9"
      ], 
      "name": "Computer Vision \u2013 ECCV 2014", 
      "type": "Book"
    }, 
    "name": "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition", 
    "pagination": "346-361", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-10578-9_23"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "90da7e6dfbf6e95b050c1e78167db4bdfa484ebf23fd9f26609cc1bb2360ee52"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1030406568"
        ]
      }
    ], 
    "publisher": {
      "location": "Cham", 
      "name": "Springer International Publishing", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-10578-9_23", 
      "https://app.dimensions.ai/details/publication/pub.1030406568"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T17:14", 
    "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_8678_00000262.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/978-3-319-10578-9_23"
  }
]
 

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.10.1007/978-3-319-10578-9_23'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10578-9_23'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10578-9_23'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-10578-9_23'


 

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

160 TRIPLES      23 PREDICATES      45 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-10578-9_23 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author N46c27ee073db4e9790ef3c5f864f6194
4 schema:citation sg:pub.10.1007/978-3-319-10584-0_26
5 sg:pub.10.1007/978-3-540-88690-7_52
6 sg:pub.10.1007/978-3-642-15561-1_11
7 https://doi.org/10.1016/j.cviu.2005.09.012
8 https://doi.org/10.1109/cvpr.2005.177
9 https://doi.org/10.1109/cvpr.2006.68
10 https://doi.org/10.1109/cvpr.2009.5206757
11 https://doi.org/10.1109/cvpr.2009.5206848
12 https://doi.org/10.1109/cvpr.2010.5540018
13 https://doi.org/10.1109/cvpr.2014.212
14 https://doi.org/10.1109/cvpr.2014.220
15 https://doi.org/10.1109/cvpr.2014.222
16 https://doi.org/10.1109/cvpr.2014.81
17 https://doi.org/10.1109/iccv.2003.1238663
18 https://doi.org/10.1109/iccv.2005.239
19 https://doi.org/10.1109/iccv.2013.10
20 https://doi.org/10.1162/neco.1989.1.4.541
21 https://doi.org/10.5244/c.25.76
22 schema:datePublished 2014
23 schema:datePublishedReg 2014-01-01
24 schema:description Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. 224×224) input image. This requirement is “artificial” and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101. The power of SPP-net is more significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method computes convolutional features 30-170× faster than the recent leading method R-CNN (and 24-64× faster overall), while achieving better or comparable accuracy on Pascal VOC 2007.
25 schema:editor N4d393a9677a347ee9098787f5c505736
26 schema:genre chapter
27 schema:inLanguage en
28 schema:isAccessibleForFree true
29 schema:isPartOf N1b96de074b854e0ca33c1e70047edfe5
30 schema:name Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
31 schema:pagination 346-361
32 schema:productId N11c6b7c3026d4da7afbde8c84da64c94
33 N6dd47509725f411fbc534b2c65343643
34 Nf477e6ebdd7d43a9a6857b2adf2ce9c9
35 schema:publisher N0a405e8a29994cdc93e818cca12c6715
36 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030406568
37 https://doi.org/10.1007/978-3-319-10578-9_23
38 schema:sdDatePublished 2019-04-15T17:14
39 schema:sdLicense https://scigraph.springernature.com/explorer/license/
40 schema:sdPublisher N22ae54741b064010b3cc87158f2015c2
41 schema:url http://link.springer.com/10.1007/978-3-319-10578-9_23
42 sgo:license sg:explorer/license/
43 sgo:sdDataset chapters
44 rdf:type schema:Chapter
45 N0a405e8a29994cdc93e818cca12c6715 schema:location Cham
46 schema:name Springer International Publishing
47 rdf:type schema:Organisation
48 N11676985f63740b985a85215d488c2db rdf:first N1be2c79e1831439cbae0a603b2bdc1c8
49 rdf:rest Nc32d70780ee248c3b6db86e7ec8d66c8
50 N11c6b7c3026d4da7afbde8c84da64c94 schema:name readcube_id
51 schema:value 90da7e6dfbf6e95b050c1e78167db4bdfa484ebf23fd9f26609cc1bb2360ee52
52 rdf:type schema:PropertyValue
53 N1b96de074b854e0ca33c1e70047edfe5 schema:isbn 978-3-319-10577-2
54 978-3-319-10578-9
55 schema:name Computer Vision – ECCV 2014
56 rdf:type schema:Book
57 N1be2c79e1831439cbae0a603b2bdc1c8 schema:familyName Pajdla
58 schema:givenName Tomas
59 rdf:type schema:Person
60 N2024f584a056479ebc3798e29f35aa2e schema:name Microsoft Research, China
61 rdf:type schema:Organization
62 N22ae54741b064010b3cc87158f2015c2 schema:name Springer Nature - SN SciGraph project
63 rdf:type schema:Organization
64 N2621b5f040384627a00bf71de00384f0 schema:affiliation https://www.grid.ac/institutes/grid.43169.39
65 schema:familyName Zhang
66 schema:givenName Xiangyu
67 rdf:type schema:Person
68 N26dd446037114dabb3057f3c9ee21cba rdf:first Ned974957e9da4328b4fcf35b682ce48a
69 rdf:rest rdf:nil
70 N46c27ee073db4e9790ef3c5f864f6194 rdf:first Nba803820319f4651bc4599a0da26610d
71 rdf:rest Nb9dc812e4bde45ab8d5bcebf72adbfa6
72 N4d393a9677a347ee9098787f5c505736 rdf:first N80f0256a8f0146fcb4400b4448cc1424
73 rdf:rest N11676985f63740b985a85215d488c2db
74 N6dd47509725f411fbc534b2c65343643 schema:name dimensions_id
75 schema:value pub.1030406568
76 rdf:type schema:PropertyValue
77 N75accaf4086a4f73b9d244ea2b438727 schema:name University of Science and Technology, China
78 rdf:type schema:Organization
79 N80f0256a8f0146fcb4400b4448cc1424 schema:familyName Fleet
80 schema:givenName David
81 rdf:type schema:Person
82 N83ceb44ccb5e4007a340f8070f38acca rdf:first Nccac75dd560c4eb29f2e65bafe5e286b
83 rdf:rest rdf:nil
84 N9a5ae020c2874b00ad5fd91805ee6b6c schema:name Microsoft Research, China
85 rdf:type schema:Organization
86 Nb9dc812e4bde45ab8d5bcebf72adbfa6 rdf:first N2621b5f040384627a00bf71de00384f0
87 rdf:rest Nd27244ed671947c39e4c6439fbaef68e
88 Nba803820319f4651bc4599a0da26610d schema:affiliation N2024f584a056479ebc3798e29f35aa2e
89 schema:familyName He
90 schema:givenName Kaiming
91 rdf:type schema:Person
92 Nc32d70780ee248c3b6db86e7ec8d66c8 rdf:first Ned0accfbbed444ab936a1939051228c4
93 rdf:rest N83ceb44ccb5e4007a340f8070f38acca
94 Nccac75dd560c4eb29f2e65bafe5e286b schema:familyName Tuytelaars
95 schema:givenName Tinne
96 rdf:type schema:Person
97 Nd27244ed671947c39e4c6439fbaef68e rdf:first Ndf3c5699e4604b02ad8243a846e52061
98 rdf:rest N26dd446037114dabb3057f3c9ee21cba
99 Ndf3c5699e4604b02ad8243a846e52061 schema:affiliation N75accaf4086a4f73b9d244ea2b438727
100 schema:familyName Ren
101 schema:givenName Shaoqing
102 rdf:type schema:Person
103 Ned0accfbbed444ab936a1939051228c4 schema:familyName Schiele
104 schema:givenName Bernt
105 rdf:type schema:Person
106 Ned974957e9da4328b4fcf35b682ce48a schema:affiliation N9a5ae020c2874b00ad5fd91805ee6b6c
107 schema:familyName Sun
108 schema:givenName Jian
109 rdf:type schema:Person
110 Nf477e6ebdd7d43a9a6857b2adf2ce9c9 schema:name doi
111 schema:value 10.1007/978-3-319-10578-9_23
112 rdf:type schema:PropertyValue
113 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
114 schema:name Information and Computing Sciences
115 rdf:type schema:DefinedTerm
116 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
117 schema:name Artificial Intelligence and Image Processing
118 rdf:type schema:DefinedTerm
119 sg:pub.10.1007/978-3-319-10584-0_26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032984348
120 https://doi.org/10.1007/978-3-319-10584-0_26
121 rdf:type schema:CreativeWork
122 sg:pub.10.1007/978-3-540-88690-7_52 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048787563
123 https://doi.org/10.1007/978-3-540-88690-7_52
124 rdf:type schema:CreativeWork
125 sg:pub.10.1007/978-3-642-15561-1_11 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045344996
126 https://doi.org/10.1007/978-3-642-15561-1_11
127 rdf:type schema:CreativeWork
128 https://doi.org/10.1016/j.cviu.2005.09.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004784969
129 rdf:type schema:CreativeWork
130 https://doi.org/10.1109/cvpr.2005.177 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093997066
131 rdf:type schema:CreativeWork
132 https://doi.org/10.1109/cvpr.2006.68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094512911
133 rdf:type schema:CreativeWork
134 https://doi.org/10.1109/cvpr.2009.5206757 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095180230
135 rdf:type schema:CreativeWork
136 https://doi.org/10.1109/cvpr.2009.5206848 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095689025
137 rdf:type schema:CreativeWork
138 https://doi.org/10.1109/cvpr.2010.5540018 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095506116
139 rdf:type schema:CreativeWork
140 https://doi.org/10.1109/cvpr.2014.212 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093810850
141 rdf:type schema:CreativeWork
142 https://doi.org/10.1109/cvpr.2014.220 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052782426
143 rdf:type schema:CreativeWork
144 https://doi.org/10.1109/cvpr.2014.222 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094012327
145 rdf:type schema:CreativeWork
146 https://doi.org/10.1109/cvpr.2014.81 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094727707
147 rdf:type schema:CreativeWork
148 https://doi.org/10.1109/iccv.2003.1238663 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094978467
149 rdf:type schema:CreativeWork
150 https://doi.org/10.1109/iccv.2005.239 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095611654
151 rdf:type schema:CreativeWork
152 https://doi.org/10.1109/iccv.2013.10 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093883984
153 rdf:type schema:CreativeWork
154 https://doi.org/10.1162/neco.1989.1.4.541 schema:sameAs https://app.dimensions.ai/details/publication/pub.1008345178
155 rdf:type schema:CreativeWork
156 https://doi.org/10.5244/c.25.76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099341617
157 rdf:type schema:CreativeWork
158 https://www.grid.ac/institutes/grid.43169.39 schema:alternateName Xi'an Jiaotong University
159 schema:name Xi’an Jiaotong University, China
160 rdf:type schema:Organization
 




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


...