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input output input input input input output output output output input output Day to Night Figure 1: Many problems in image processing, graphics, and vision involve translating an input image into a corresponding output image. These problems are often treated with application-specific algorithms, even though the setting is always the same: map pixels to(More)
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated col-orizations. We propose a fully automatic approach that produces vibrant and(More)
Detecting boundaries between semantically meaningful objects in visual scenes is an important component of many vision algorithms. In this paper, we propose a novel method for detecting such boundaries based on a simple underlying principle: pixels belonging to the same object exhibit higher statistical dependencies than pixels belonging to different(More)
When glancing at a magazine, or browsing the Internet, we are continuously being exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stitch to our minds, and other are forgotten. In this paper(More)
Zebras Horses horse zebra zebra horse Summer Winter summer winter winter summer Photograph Van Gogh Cezanne Monet Ukiyo-e Monet Photos Monet photo photo Monet Figure 1: Given any two unordered image collections X and Y , our algorithm learns to automatically " translate " an image from one into the other and vice versa: (left) 1074 Monet paintings and 6753(More)
When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stick in our minds while others are quickly forgotten. In this(More)
Artists, advertisers, and photographers are routinely presented with the task of creating an image that a viewer will remember. While it may seem like image memorability is purely subjective, recent work shows that it is not an inexplicable phenomenon: variation in memorability of images is consistent across subjects, suggesting that some images are(More)
The faces we encounter throughout our lives make different impressions on us: Some are remembered at first glance, while others are forgotten. Previous work has found that the distinctiveness of a face influences its memorability--the degree to which face images are remembered or forgotten. Here, we generalize the concept of face memorability in a(More)
To quickly synthesize complex scenes, digital artists often collage together visual elements from multiple sources: for example, mountains from New Zealand behind a Scottish castle with wisps of Saharan sand in front. In this paper, we propose to use a similar process in order to parse a scene. We model a scene as a collage of warped, layered objects(More)
We propose a framework that infers mid-level visual properties of an image by learning about ordinal relationships. Instead of estimating metric quantities directly, the system proposes pairwise relationship estimates for points in the input image. These sparse probabilistic ordinal measurements are globalized to create a dense output map of continuous(More)