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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...
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...
Object detection from images is generally achieved through a supervised learning manner. However, in many real applications, to provide instance level label is still costly. Thus, weakly supervised approach is proposed and naturally cast as a Multiple Instance Learning (MIL) problem. Traditional MIL methods typically learn discriminative classifiers from positive and negative training bags. Alternatively,...
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