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• W2952907227 abstract "This work presents an unsupervised learning based approach to the ubiquitous computer vision problem of image matching. We start from the insight that the problem of frame-interpolation implicitly solves for inter-frame correspondences. This permits the application of analysis-by-synthesis: we firstly train and apply a Convolutional Neural Network for frame-interpolation, then obtain correspondences by inverting the learned CNN. The key benefit behind this strategy is that the CNN for frame-interpolation can be trained in an unsupervised manner by exploiting the temporal coherency that is naturally contained in real-world video sequences. The present model therefore learns image matching by simply watching videos. Besides a promise to be more generally applicable, the presented approach achieves surprising performance comparable to traditional empirically designed methods." @default.
• W2952907227 created "2019-06-27" @default.
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• W2952907227 date "2016-03-18" @default.
• W2952907227 modified "2023-10-18" @default.
• W2952907227 title "Learning Image Matching by Simply Watching Video" @default.
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• W2952907227 doi "https://doi.org/10.48550/arxiv.1603.06041" @default.
• W2952907227 hasPublicationYear "2016" @default.
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