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Predicting Ground-Level Scene Layout from Aerial Imagery
We introduce a novel strategy for learning to extract semantically meaningful features from aerial imagery. Instead of manually labeling the aerial imagery, we propose to predict (noisy) semanticExpand
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Horizon Lines in the Wild
The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. TheseExpand
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Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld
We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidateExpand
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A Unified Model for Near and Remote Sensing
We propose a novel convolutional neural network architecture for estimating geospatial functions such as population density, land cover, or land use. In our approach, we combine overhead andExpand
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Learning to Look around Objects for Top-View Representations of Outdoor Scenes
Given a single RGB image of a complex outdoor road scene in the perspective view, we address the novel problem of estimating an occlusion-reasoned semantic scene layout in the top-view. ThisExpand
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DEEPFOCAL: A method for direct focal length estimation
Estimating the focal length of an image is an important preprocessing step for many applications. Despite this, existing methods for single-view focal length estimation are limited in that theyExpand
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A Multimodal Approach to Mapping Soundscapes
We explore the problem of mapping soundscapes, that is, predicting the types of sounds that are likely to be heard at a given geographic location. Using a novel dataset, which includes geo-taggedExpand
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Analyzing human appearance as a cue for dating images
Given an image, we propose to use the appearance of people in the scene to estimate when the picture was taken. There are a wide variety of cues that can be used to address this problem. MostExpand
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A fast method for estimating transient scene attributes
We propose the use of deep convolutional neural networks to estimate the transient attributes of a scene from a single image. Transient scene attributes describe both the objective conditions, suchExpand
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Learning Geo-Temporal Image Features
We propose to implicitly learn to extract geo-temporal image features, which are mid-level features related to when and where an image was captured, by explicitly optimizing for a set of location andExpand
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