Multi-fold MIL Training for Weakly Supervised Object Localization

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CİNBİŞ R. G. , Verbeek J., Schmid C.

27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Ohio, United States Of America, 23 - 28 June 2014, pp.2409-2416 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/cvpr.2014.309
  • City: Ohio
  • Country: United States Of America
  • Page Numbers: pp.2409-2416


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 high-dimensional representations, such as the Fisher vectors, are used. We present a detailed experimental evaluation using the PASCAL VOC 2007 dataset. Compared to state-of-the-art weakly supervised detectors, our approach better localizes objects in the training images, which translates into improved detection performance.