2018
DOI: 10.1007/978-3-030-01264-9_13
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Single Image Intrinsic Decomposition Without a Single Intrinsic Image

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Cited by 61 publications
(55 citation statements)
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“…Self-supervised learning. Our work builds off efforts to learn perceptual models that are "self-supervised" by leveraging natural contextual signals in images [10,22,33,38,24], videos [46,32,43,44,13,20], and even radio signals [48]. These approaches utilize the power of supervised learning while not requiring manual annotations, instead deriving supervisory signals from the structure in Procedure to generate the sound of a pixel: pixel-level visual features are extracted by temporal max-pooling over the output of a dilated ResNet applied to T frames.…”
Section: Related Workmentioning
confidence: 99%
“…Self-supervised learning. Our work builds off efforts to learn perceptual models that are "self-supervised" by leveraging natural contextual signals in images [10,22,33,38,24], videos [46,32,43,44,13,20], and even radio signals [48]. These approaches utilize the power of supervised learning while not requiring manual annotations, instead deriving supervisory signals from the structure in Procedure to generate the sound of a pixel: pixel-level visual features are extracted by temporal max-pooling over the output of a dilated ResNet applied to T frames.…”
Section: Related Workmentioning
confidence: 99%
“…Most recently, unsupervised methods have been presented in which the training is performed on image sequences taken from fixed-position, time-lapse video with varying illumination (Li and Snavely 2018b;Ma et al 2018). In these networks, a major source of guidance for unsupervised training is the temporal consistency of reflectance for static regions within a sequence.…”
Section: Related Workmentioning
confidence: 99%
“…Our proposed system also trains on multiple images in an unsupervised manner and can be applied at test time on single images. Different from the previous fixed-view multi-image techniques (Li and Snavely 2018b;Ma et al 2018), our network uses unconstrained multi-view images and deals specifically with misalignment issues that arise in this setting. Such image sequences from unconstrained random views are much easier to obtain than fixed-view images.…”
Section: Related Workmentioning
confidence: 99%
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