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Conference Papers Year : 2014

Multi-fold MIL Training for Weakly Supervised Object Localization

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Ramazan Gokberk Cinbis
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Jakob Verbeek
Cordelia Schmid
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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.
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Dates and versions

hal-00975746 , version 1 (09-04-2014)



Ramazan Gokberk Cinbis, Jakob Verbeek, Cordelia Schmid. Multi-fold MIL Training for Weakly Supervised Object Localization. CVPR - IEEE Conference on Computer Vision & Pattern Recognition, Jun 2014, Columbus, United States. pp.2409-2416, ⟨10.1109/CVPR.2014.309⟩. ⟨hal-00975746⟩
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