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A metric for natural image patches is an important tool for analyzing images. An efficient means of learning one is to train a deep network to map an image patch to a vector space, in which the Euclidean distance reflects patch similarity. Previous attempts learned such an embedding in a supervised manner, requiring the availability of many annotated images. In this paper, we present an unsupervised...
We propose a new computer vision task we call “distractor prediction.” Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing distractors—for example, using in-painting — can improve the composition of an image. In this work we created two datasets of images with user annotations to identify the characteristics of distractors...
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