In this paper, we study the partial multi-label (PML) image classification problem, where each image is annotated with a candidate label set consists of multiple relevant labels and other noisy labels. Existing PML methods typically design a disambiguation strategy to filter out noisy labels by utilizing prior knowledge with extra assumptions, which unfortunately is unavailable in many real tasks. Furthermore, because the objective function for disambiguation is usually elaborately designed on the whole training set, it can be hardly optimized in a deep model with SGD on mini-batches. In this paper, for the first time we propose a deep model for PML to enhance the representation and discrimination ability. On one hand, we propose a novel curriculum based disambiguation strategy to progressively identify ground-truth labels by incorporating the varied difficulties of different classes. On the other hand, a consistency regularization is introduced for model retraining to balance fitting identified easy labels and exploiting potential relevant labels. Extensive experimental results on the commonly used benchmark datasets show the proposed method significantly outperforms the SOTA methods.

翻译：本文研究偏多多标签（PML）图像分类问题，其中每张图像被标注一个由多个相关标签及其他噪声标签组成的候选标签集。现有PML方法通常通过利用带有额外假设的先验知识设计消歧策略来过滤噪声标签，然而这在许多实际任务中难以获取。此外，由于消歧的目标函数通常针对整个训练集精心设计，难以通过基于小批量随机梯度下降的深度模型进行优化。本文首次提出用于PML的深度模型以增强表征与判别能力。一方面，我们提出一种新颖的基于课程式消歧策略，通过融入不同类别的难度差异逐步识别真实标签；另一方面，引入一致性正则化用于模型再训练，以平衡拟合已识别的简单标签与挖掘潜在相关标签。在常用基准数据集上的大量实验结果表明，所提方法显著优于现有最优方法。