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- W4313304490 abstract "Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge the most the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against other display strategies as well as the related work." @default.
- W4313304490 created "2023-01-06" @default.
- W4313304490 creator A5029896607 @default.
- W4313304490 creator A5052932586 @default.
- W4313304490 date "2022-12-28" @default.
- W4313304490 modified "2023-09-23" @default.
- W4313304490 title "Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection" @default.
- W4313304490 doi "https://doi.org/10.48550/arxiv.2212.13974" @default.
- W4313304490 hasPublicationYear "2022" @default.
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