[Doc] update rtformer new in readme

PaddlePaddle · Nov 30, 2022 · 19351ba · 19351ba
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diff --git a/README_EN.md b/README_EN.md
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 <ul class="nobull">
     <li>[2022-11-30] :fire: PaddleSeg v2.7 is released! More details in <a href="https://github.com/PaddlePaddle/PaddleSeg/releases">Release Notes</a>.</li>
     <ul>
-        <li>Release <a href="./Matting/">PP-MattingV2</a>, a real-time human matting model with SOTA performance. Compared to previous models, the mean error is reduced by 17.91%, the inference speed is improved by 44.6% on GPU. </li>
+        <li>Release <a href="./Matting/">PP-MattingV2</a>, a real-time human matting model with SOTA performance. Compared to previous models, the mean error is reduced by 17.91%, the inference speed is accelerated by 44.6% on GPU. </li>
         <li>Release <a href="./contrib/MedicalSeg/">MedicalSegV2</a>, a superior 3D medical image segmentation solution, including an intelligent annotation toolkit called EISeg-Med3D, several state-of-the-art models and an optimized nnUNet-D with high performance.</li>
-        <li>Officially release <a href="./configs/rtformer/">RTFormer</a>, a real-time semantic segmentation model. It is proposed by Baidu and accepted by NeurIPS 2022.
+        <li>Release <a href="./configs/rtformer/">RTFormer</a>, a real-time semantic segmentation model accepted by NeurIPS 2022. RTFormer combines the advantages of CNN and Transformer modules, and it achieves SOTA trade-off between performance and efficiency on several datasets.</li>
     </ul>
   <li>[2022-07-20] PaddleSeg v2.6 released a real-time human segmentation SOTA solution <a href="./contrib/PP-HumanSeg">PP-HumanSegV2</a>,  a stable-version semi-automatic segmentation annotation <a href="./EISeg">EISeg v1.0</a>, a pseudo label pre-training method PSSL and the source code of PP-MattingV1. </li>
   <li>[2022-04-20] PaddleSeg v2.5 released a real-time semantic segmentation model <a href="./configs/pp_liteseg">PP-LiteSeg</a>, a trimap-free image matting model PP-MattingV1, and an easy-to-use solution for 3D medical image segmentation MedicalSegV1.</li>