论文标题
基于Intra Intra主题一致性的地标的无监督学习
Unsupervised Learning of Landmarks based on Inter-Intra Subject Consistencies
论文作者
论文摘要
我们提出了一种新颖的无监督学习方法，以通过在面部图像上纳入受试者间地标的一致性来形象地标发现。这是通过受主体间映射模块实现的，该模块基于辅助主题相关的结构来转换原始主题地标。为了从转换的图像恢复回原始主题，地标探测器被迫学习配对配对内图像以及配对的主体间图像之间包含一致语义含义的空间位置。我们提出的方法在具有各种设置的两个公共面部图像数据集（MAFL，AFLW）上进行了广泛评估。实验结果表明，与先前的定量和质量上的最新方法相比，我们的方法可以提取两个数据集的一致地标，并取得更好的性能。
We present a novel unsupervised learning approach to image landmark discovery by incorporating the inter-subject landmark consistencies on facial images. This is achieved via an inter-subject mapping module that transforms original subject landmarks based on an auxiliary subject-related structure. To recover from the transformed images back to the original subject, the landmark detector is forced to learn spatial locations that contain the consistent semantic meanings both for the paired intra-subject images and between the paired inter-subject images. Our proposed method is extensively evaluated on two public facial image datasets (MAFL, AFLW) with various settings. Experimental results indicate that our method can extract the consistent landmarks for both datasets and achieve better performances compared to the previous state-of-the-art methods quantitatively and qualitatively.
