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Convolutional neural networks (CNN) have recently shown outstanding image classification performance in the large-scale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich mid-level image representations as opposed to hand-designed low-level features used in other image classification methods. Learning(More)
Successful methods for visual object recognition typically rely on training datasets containing lots of richly annotated images. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective. We describe a weakly supervised convolutional neu-ral network (CNN) for object classification that relies only on(More)
Part II: Ranked images and corresponding score maps for Pascal VOC 2012 action recognition test set In this document we show example images from Pascal VOC 2012 test set sorted by the decreasing score of the convolutional neural network object classifier for the 10 Pascal VOC action classes. Note that only every 5th image (when sorted by the decreasing(More)
We aim to localize objects in images using image-level supervision only. Previous approaches to this problem mainly focus on discriminative object regions and often fail to locate precise object boundaries. We address this problem by introducing two types of context-aware guidance models, additive and contrastive models, that leverage their surrounding(More)
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