%0 Journal Article
%T 语言驱动的语义边缘检测
Language-Driven Semantic Edge Detection
%A 余斌
%A 张笑钦
%A 邓若曦
%J Computer Science and Application
%P 169-178
%@ 2161-881X
%D 2025
%I Hans Publishing
%R 10.12677/csa.2025.152044
%X 语义边缘检测致力于精确描绘对象边界并为各个像素分配类别标签，这对实现准确定位和分类提出了双重挑战。本研究介绍了语言驱动语义边缘检测，这是一个简单的框架，可增强语义轮廓检测模型。语言驱动语义边缘检测旨在利用嵌入在文本表示中的语义信息来重新校准边缘检测器的注意力，从而增强高级图像特征的判别能力。为了实现这一点，我们引入了文本特征信息，使用跨模态融合方式增强了边缘检测器的定位和分类。在SBD和CityScapes数据集上的实验结果表明，模型性能得到显著提升。例如，在CASENet中加入文本特征信息可将SBD数据集上的平均ODS得分从70.4提高到72.6。最终，语言驱动语义边缘检测实现了领先的平均ODS 77.0，超越了竞争对手。我们将展示更多额外的结合方法、主干网络的效果。
Semantic edge detection strives to accurately delineate object boundaries and assign category labels to individual pixels, which poses a dual challenge to achieve accurate localization and classification. This study introduces language-driven semantic edge detection, a simple framework that enhances semantic contour detection models. Language-driven semantic edge detection aims to leverage the semantic information embedded in text representations to recalibrate the attention of edge detectors, thereby enhancing the discriminative ability of high-level image features. To achieve this, we introduce text feature information and use cross-modal fusion to enhance the localization and classification of edge detectors. Experimental results on SBD and CityScapes datasets show that model performance is significantly improved. For example, adding text feature information to CASENet improves the average ODS score on the SBD dataset from 70.4 to 72.6. Ultimately, language-driven semantic edge detection achieves a leading average ODS of 77.0, surpassing the competition. We will show the effects of more additional combining methods and backbone networks.
%K 语义边缘检测，
%K 跨模态融合，
%K 卷积神经网络，
%K CLIP
Semantic Edge Detection
%K Cross-Modal Fusion
%K Convolutional Neural Network
%K CLIP
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=108342