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While recent advances in deep learning pushed the state-of-the-art in object detection and semantic segmentation, it often comes at the cost of a considerable annotation effort. Thus, weakly supervised learning became of increasing interest. In this paper a novel approach to the challenging task of weakly supervised segmentation and object localization will be presented. The problem is tackled from...
In this paper a bottom-up approach for detecting and recognizing objects in complex scenes is presented. In contrast to top-down methods, no prior knowledge about the objects is required beforehand. Instead, two different views on the data are computed: First, a GIST descriptor is used for clustering scenes with a similar global appearance which produces a set of Proto-Scenes. Second, a visual attention...
Handwritten historical documents pose extremely challenging problems for automatic analysis. This is due to the high variability observed in handwritten script, the use of writing styles and script types unknown today, the frequently lacking orthographic standardization, and the degradation of the respective documents. Therefore, it is currently out of question to develop general purpose handwriting...
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