https://vilab.team/tag/%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87/ 超分辨率 Hugo Blox Builder (https://hugoblox.com)en-usSun, 19 Oct 2025 00:00:00 +0000 https://vilab.team/media/icon_hu2896232876136423579.png https://vilab.team/tag/%E8%B6%85%E5%88%86%E8%BE%A8%E7%8E%87/ https://vilab.team/publication/hybrid-vision-transformer-and-convolutional-neural-network-f/ Sun, 19 Oct 2025 00:00:00 +0000 https://vilab.team/publication/hybrid-vision-transformer-and-convolutional-neural-network-f/ <p>本文针对超分辨率图像质量评估（SRIQA）任务，提出一种结合视觉Transformer（ViT）与卷积神经网络（CNN）的混合无参考评估模型。该方法利用ViT提取非局部特征，并通过多阶段自注意力处理图像令牌，再将其重塑为特征图，由CNN编码映射为质量分数，从而同时捕捉局部与非局部信息，克服了传统全参考指标依赖真实图像且可靠性不足的问题。</p> https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/ Sun, 19 Oct 2025 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/ <p>热烈祝贺李欣羽同学！论文《Hybrid Vision Transformer and Convolutional Neural Network for Super-Resolution Image Quality Assessment》已发表在 <em>ICCV Workshops</em>。</p> <h2 id="hybrid-vision-transformer-and-convolutional-neural-network-for-super-resolution-image-quality-assessment">Hybrid Vision Transformer and Convolutional Neural Network for Super-Resolution Image Quality Assessment</h2> <p><strong>祝贺李欣羽同学！该论文已发表在 <em>ICCV Workshops</em>。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="Hybrid Vision Transformer and Convolutional Neural Network for Super-Resolution Image Quality Assessment" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/images/paper-01_hu4220616556499505728.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/images/paper-01_hu4217849018483358210.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/images/paper-01_hu5826829679943132540.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iccv-workshops/images/paper-01_hu4220616556499505728.webp" width="760" height="329" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Xinyu Li、Chuanbiao Song、Chenqi Zhang、Jun Lan、Huijia Zhu、Weiqiang Wang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> <em>ICCV Workshops</em></li> <li><strong>发表时间：</strong> 2025年10月19日</li> <li><strong>相关链接：</strong> <a href="https://ieeexplore.ieee.org/abstract/document/11375208/" target="_blank" rel="noopener">论文链接</a> · <a href="https://openaccess.thecvf.com/content/ICCV2025W/VQualA/papers/Li_Hybrid_Vision_Transformer_and_Convolutional_Neural_Network_for_Super-Resolution_Image_ICCVW_2025_paper.pdf" target="_blank" rel="noopener">PDF</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文针对超分辨率图像质量评估（SRIQA）任务，提出一种结合视觉Transformer（ViT）与卷积神经网络（CNN）的混合无参考评估模型。该方法利用ViT提取非局部特征，并通过多阶段自注意力处理图像令牌，再将其重塑为特征图，由CNN编码映射为质量分数，从而同时捕捉局部与非局部信息，克服了传统全参考指标依赖真实图像且可靠性不足的问题。</p>