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      Weakly supervised object localization with multi-fold multiple instance learning

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      Author
      Cinbis, R. G.
      Verbeek, J.
      Schmid, C.
      Date
      2017
      Source Title
      IEEE Transactions on Pattern Analysis and Machine Intelligence
      Print ISSN
      0162-8828
      Publisher
      IEEE Computer Society
      Volume
      39
      Issue
      1
      Pages
      189 - 203
      Language
      English
      Type
      Article
      Item Usage Stats
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      190
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      Abstract
      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. © 2016 IEEE.
      Keywords
      Object detection
      Weakly supervised learning
      Computer vision
      Iterative methods
      Learning systems
      Location
      Locks (fasteners)
      Neural networks
      Supervised learning
      Convolutional neural network
      Experimental evaluation
      Localization accuracy
      Multiple instance learning
      Object localization
      Refinement methods
      Supervised trainings
      Object recognition
      Permalink
      http://hdl.handle.net/11693/37100
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/TPAMI.2016.2535231
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      • Department of Computer Engineering 1368
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