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Learning object detection models from weakly labeled data is an important topic in computer vision. Among various types of weak annotations, image-level object labeling is a natural one that tells the existence, but not the precise locations, of object instances in images. Learning object detectors from image-level labels can be naturally cast as a multiple instance learning (MIL) problem. Existing...
Recently, privacy protection attracts more and more research efforts especially for social network data. In the literature, most of existing approaches assume that social data are independent to each other which is not the case in the real world applications. In this paper, we are the first attempt to investigate the privacy protection issue when social data are correlated. We model the correlation...
In this paper, we address the problem of learning object class models from weakly labeled training images, where labels of object classes are only provided at image level. Such weakly supervised object learning can be considered as a Multiple Instance Learning (MIL) problem. We observed that object instances of a common category are visually similar and when characterized as high-dimensional feature...
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