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|e rakwb
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|a eng
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| 100 |
1 |
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|a Cinbis, Ramazan Gokberk
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning
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| 264 |
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|c 2017
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| 336 |
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 06.08.2018
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| 500 |
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|a Date Revised 06.08.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Object category localization is a challenging problem in computer vision. Standard supervised training requires bounding box annotations of object instances. This time-consuming annotation process is sidestepped in weakly supervised learning. In this case, the supervised information is restricted to binary labels that indicate the absence/presence of object instances in the image, without their locations. We follow a multiple-instance learning approach that iteratively trains the detector and infers the object locations in the positive training images. Our main contribution is a multi-fold multiple instance learning procedure, which prevents training from prematurely locking onto erroneous object locations. This procedure is particularly important when using high-dimensional representations, such as Fisher vectors and convolutional neural network features. We also propose a window refinement method, which improves the localization accuracy by incorporating an objectness prior. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset, which verifies the effectiveness of our approach
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| 650 |
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Verbeek, Jakob
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Schmid, Cordelia
|e verfasserin
|4 aut
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| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 1 vom: 01. Jan., Seite 189-203
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:39
|g year:2017
|g number:1
|g day:01
|g month:01
|g pages:189-203
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|d 39
|j 2017
|e 1
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|h 189-203
|