Single-level feature-to-feature forecasting with deformable convolutions on coarse abstract features from a segmentation backbone achieves state-of-the-art results for nine-timestep future semantic segmentation on Cityscapes validation.
IEEE transactions on pattern analysis and machine intelli- gence 40(4), 834–848 (2018)
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ASCNet learns per-pixel adaptive dilation rates via a 3-layer convolution structure to produce scale-appropriate receptive fields, yielding higher segmentation accuracy than fixed dilated CNNs on two medical image datasets.
citing papers explorer
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Single Level Feature-to-Feature Forecasting with Deformable Convolutions
Single-level feature-to-feature forecasting with deformable convolutions on coarse abstract features from a segmentation backbone achieves state-of-the-art results for nine-timestep future semantic segmentation on Cityscapes validation.
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ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning
ASCNet learns per-pixel adaptive dilation rates via a 3-layer convolution structure to produce scale-appropriate receptive fields, yielding higher segmentation accuracy than fixed dilated CNNs on two medical image datasets.