Cinbis, R. G.Verbeek, J.Schmid, C.2018-04-122018-04-1220170162-8828http://hdl.handle.net/11693/37100Object 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.EnglishObject detectionWeakly supervised learningComputer visionIterative methodsLearning systemsLocationLocks (fasteners)Neural networksSupervised learningConvolutional neural networkExperimental evaluationLocalization accuracyMultiple instance learningObject localizationRefinement methodsSupervised trainingsObject recognitionArticle10.1109/TPAMI.2016.2535231