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• W2910788835 abstract "We propose an adversarial contextual model for detecting moving objects in images. A deep neural network is trained to predict the optical flow in a region using information from everywhere else but that region (context), while another network attempts to make such context as uninformative as possible. The result is a model where hypotheses naturally compete with no need for explicit regularization or hyper-parameter tuning. Although our method requires no supervision whatsoever, it outperforms several methods that are pre-trained on large annotated datasets. Our model can be thought of as a generalization of classical variational generative region-based segmentation, but in a way that avoids explicit regularization or solution of partial differential equations at run-time." @default.
• W2910788835 created "2019-01-25" @default.
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• W2910788835 date "2019-01-10" @default.
• W2910788835 modified "2023-09-23" @default.
• W2910788835 title "Unsupervised Moving Object Detection via Contextual Information Separation" @default.
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• W2910788835 doi "https://doi.org/10.48550/arxiv.1901.03360" @default.
• W2910788835 hasPublicationYear "2019" @default.
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