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Status: Unchecked ×Keyword: Pascal Context ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

   Zheng, Lu, Zhao et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021

   The paper treats semantic segmentation as sequence-to-sequence prediction, using a pure transformer encoder with a simple decoder (SETR), and reports strong results on standard benchmarks.

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   1. Unchecked“With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR).”
   2. UncheckedThe SETR transformer model reports state-of-the-art scores on ADE20K and Pascal Context, and competitive results on Cityscapes, for semantic segmentation.“Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes.”