Multi-label image recognition is a fundamental yet practical task because real-world images inherently possess multiple semantic labels. However, it is difficult to collect large-scale multi-label annotations due to the complexity of both the input images and output label spaces. To reduce the annotation cost, we propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels, i.e., merely some labels are known while other labels are missing (also called unknown labels) per image. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations to transfer knowledge of known labels to generate pseudo labels for unknown labels. Specifically, an intra-image semantic transfer module learns image-specific label co-occurrence matrix and maps the known labels to complement unknown labels based on this matrix. Meanwhile, a cross-image transfer module learns category-specific feature similarities and helps complement unknown labels with high similarities. Finally, both known and generated labels are used to train the multi-label recognition models. Extensive experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms. Codes are available at \url{https://github.com/HCPLab-SYSU/SST-MLR-PL

提出了一种结构化的语义转移（SST）框架，用于训练多标签识别模型，其特点是仅知道某些标签而丢失了其他标签。该框架包括两个互补的转移模块，探索图像内部和跨图像的语义相关性，以生成未知标签的伪标签，并使用已知和生成的标签来训练多标签识别模型。实验表明，该框架在多个数据集上实现了优越的性能。

使用部分标签进行多标签识别的结构化语义转移