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In this work, we develop a new convolutional network\nmodule that is specifically designed for dense prediction. The presented module\nuses dilated convolutions to systematically aggregate multi-scale contextual\ninformation without losing resolution. The architecture is based on the fact\nthat dilated convolutions support exponential expansion of the receptive field\nwithout loss of resolution or coverage. We show that the presented context\nmodule increases the accuracy of state-of-the-art semantic segmentation\nsystems. 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