论文标题
对称性感知变压器的镜像检测
Symmetry-Aware Transformer-based Mirror Detection
论文作者
论文摘要
镜像检测旨在识别给定输入图像中的镜像区域。现有作品主要集中于将语义特征和结构特征集成到镜像和非摩尔区域之间的特定关系，或引入镜像属性，例如深度或手性，以帮助分析镜像的存在。在这项工作中，我们观察到一个真实的对象通常与镜子中的相应反射形成松散的对称关系，这有助于区分镜子和真实对象。基于此观察结果，我们提出了一个基于双路对称性变压器的镜像检测网络（SATNET），其中包括两个新型模块：对称性吸引注意的注意模块（SAAM）以及对比度和融合解码器模块（CFDM）。具体而言，我们首先采用变压器骨干来对图像中的全局信息聚合进行建模，从而在两个路径中提取多尺度特征。然后，我们将高级双路径特征馈送到Saams以捕获对称关系。最后，我们融合了双路径特征，并使用CFDM逐渐完善我们的预测图，以获得最终的镜面掩码。实验结果表明，在所有可用的镜像检测数据集上，卫星的表现都优于RGB和RGB-D镜检测方法。代码和训练有素的模型可在以下网址找到：https：//github.com/tyhuang0428/satnet。
Mirror detection aims to identify the mirror regions in the given input image. Existing works mainly focus on integrating the semantic features and structural features to mine specific relations between mirror and non-mirror regions, or introducing mirror properties like depth or chirality to help analyze the existence of mirrors. In this work, we observe that a real object typically forms a loose symmetry relationship with its corresponding reflection in the mirror, which is beneficial in distinguishing mirrors from real objects. Based on this observation, we propose a dual-path Symmetry-Aware Transformer-based mirror detection Network (SATNet), which includes two novel modules: Symmetry-Aware Attention Module (SAAM) and Contrast and Fusion Decoder Module (CFDM). Specifically, we first adopt a transformer backbone to model global information aggregation in images, extracting multi-scale features in two paths. We then feed the high-level dual-path features to SAAMs to capture the symmetry relations. Finally, we fuse the dual-path features and refine our prediction maps progressively with CFDMs to obtain the final mirror mask. Experimental results show that SATNet outperforms both RGB and RGB-D mirror detection methods on all available mirror detection datasets. Codes and trained models are available at: https://github.com/tyhuang0428/SATNet.
