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.
The handbook of brain theory and neural networks 3361(10), 1995 (1995)
2 Pith papers cite this work. Polarity classification is still indexing.
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A DL model pre-trained on the Human Connectome Project dataset achieves 67.51% accuracy decoding cognitive states from a new fMRI task after fine-tuning on data from only three subjects.
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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Deep Transfer Learning For Whole-Brain fMRI Analyses
A DL model pre-trained on the Human Connectome Project dataset achieves 67.51% accuracy decoding cognitive states from a new fMRI task after fine-tuning on data from only three subjects.