Deep ConvNets have shown great performance for single-label image classification (e.g. ImageNet), but it is necessary to move beyond the single-label classification task because pictures of everyday life are inherently multi-label. Multi-label classification is a more difficult task than single-label classification because both the input images and output label spaces are more complex. Furthermore, collecting clean multi-label annotations is more difficult to scale-up than single-label annotations. To reduce the annotation cost, we propose to train a model with partial labels i.e. only some labels are known per image. We first empirically compare different labeling strategies to show the potential for using partial labels on multi-label datasets. Then to learn with partial labels, we introduce a new classification loss that exploits the proportion of known labels per example. Our approach allows the use of the same training settings as when learning with all the annotations. We further explore several curriculum learning based strategies to predict missing labels. Experiments are performed on three large-scale multi-label datasets: MS COCO, NUS-WIDE and Open Images.

该研究提出了一种使用部分标签进行多标签分类的方法，其中新的分类损失利用了每个示例已知标签的比例，并通过课程学习的策略预测遗漏标签。通过实验发现，该方法在MS COCO、NUS-WIDE和Open Images等三个大规模多标签数据集上取得了良好表现。

使用部分标签学习深度卷积神经网络进行多标签分类