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The task of semantic segmentation is to infer a predefined category label for each pixel in the image. For most cases, image segmentation is established as a fully supervised task. These methods all built on the basis of having access to sufficient pixel-wise annotated samples for training. However, obtaining the satisfied ground truth is not only labor intensive but also time-consuming, which severely...
Inferring the aesthetic quality of images is a challenging computer vision task due to its subjective and conceptual nature. Most image aesthetics evaluation approaches focused on designing handcrafted features, and only a few adopted learning of relevant and imperative characteristics in a data-driven manner. In this paper, we propose to attune Convolutional Neural Networks (CNNs) for image aesthetics...
The use of top-down categorization information in bottom-up semantic segmentation can significantly improve its performance. The basic Conditional Random Field (CR-F) model can capture the local contexture information, while the locality-consistent sparse representation can obtain the category-level priors and the relationship infeature space. In this paper, we propose a novel semantic segmentation...
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