Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning

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2017-01-01
Cinbiş, Ramazan Gökberk
Schmid, Cordelia
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 PASCALVOC 2007 dataset, which verifies the effectiveness of our approach.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

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Citation Formats
R. G. Cinbiş and C. Schmid, “Weakly Supervised Object Localization with Multi-Fold Multiple Instance Learning,” IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, pp. 189–203, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42242.