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Conference papers

Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection

Abstract : Large scale datasets created from user labels or openly available data have become crucial to provide training data for large scale learning algorithms. While these datasets are easier to acquire, the data are frequently noisy and unreliable , which is motivating research on weakly supervised learning techniques. In this paper we propose an iterative learning method that extracts the useful information from a large scale change detection dataset generated from open vector data to train a fully convolutional network which surpasses the performance obtained by naive supervised learning. We also propose the guided anisotropic diffusion algorithm , which improves semantic segmentation results using the input images as guides to perform edge preserving filtering , and is used in conjunction with the iterative training method to improve results.
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Contributor : Rodrigo Caye Daudt Connect in order to contact the contributor
Submitted on : Wednesday, December 4, 2019 - 10:30:38 AM
Last modification on : Tuesday, January 18, 2022 - 3:28:04 PM
Long-term archiving on: : Thursday, March 5, 2020 - 5:05:16 PM


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  • HAL Id : hal-02392779, version 1


Rodrigo Daudt, Bertrand Le Saux, Alexandre Boulch, Yann Gousseau. Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection. Computer Vision and Pattern Recognition Workshops, Jun 2019, Long Beach, United States. ⟨hal-02392779⟩



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