May 24, 2017 · In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer ...
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Dense Transformer Networks can extract features based on irregular areas, whose shapes and sizes are based on data. In the meantime, Dense Transformer Networks ...
The dense transformer networks employ an encoder-decoder architecture, and a pair of dense transformer modules are inserted into each of the encoder and decoder.
Abstract. The key idea of current deep learning methods for dense prediction is to apply a model on a regu- lar patch centered on each pixel to make pixel-.
Jul 24, 2023 · We proposed a novel Dense Transformer based Enhanced Coding Network (DTEC-Net) for unsupervised metal artifact reduction.
... dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an encoder-decoder architecture ...
May 2, 2024 · Dense Prediction Transformers (DPT) are a type of deep learning model designed for tasks that require dense, pixel-level predictions, such as ...
Dec 17, 2018 · I am trying to implement stn. The affine_grid in torch.nn supports global transformation (for the whole image), ie theta is Nx2x3 but I am looking for an ...
Mar 28, 2024 · In the realm of image processing, Dense Transformer Networks have emerged as a powerful tool for enhancing visual data.
In this work, we propose the dense transformer networks, which can learn the shapes and sizes of patches from data. The dense transformer networks employ an ...