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      RETRACTED: Transformer-induced graph reasoning for multimodal semantic segmentation in remote sensing

      , , , , ,
      ISPRS Journal of Photogrammetry and Remote Sensing
      Elsevier BV

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          Deep Residual Learning for Image Recognition

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            Attention Is All You Need

            The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. 15 pages, 5 figures
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              Fully convolutional networks for semantic segmentation

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                Author and article information

                Journal
                ISPRS Journal of Photogrammetry and Remote Sensing
                ISPRS Journal of Photogrammetry and Remote Sensing
                Elsevier BV
                09242716
                November 2022
                November 2022
                : 193
                : 90-103
                Article
                10.1016/j.isprsjprs.2022.08.010
                a1915621-025a-413b-afe5-665ff6352f38
                © 2022

                https://www.elsevier.com/tdm/userlicense/1.0/

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