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Semantic segmentation has been a long standing challenging task in computer vision. It aims at assigning a label to each image pixel and needs a significant number of pixel-level annotated data, which is often unavailable. To address this lack of annotations, in this paper, we leverage, on one hand, a massive amount of available unlabeled or weakly labeled data, and on the other hand, non-real images...
Scene labeling task is to segment the image into meaningful regions and categorize them into classes of objects which comprised the image. Commonly used methods typically find the local features for each segment and label them using classifiers. Afterwards, labeling is smoothed in order to make sure that neighboring regions receive similar labels. However, these methods ignore expressive connections...
Visual saliency is the ability of a vision system to promptly select the most relevant data in the scene and reduce the amount of visual data that needs to be processed. Thus, its applications for complex tasks such as object detection, object recognition and video compression have attained interest in computer vision studies. In this paper, we introduce a novel unsupervised method for detecting visual...
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