In many real-world tasks, the concerned objects can be represented as a multi-instance bag associated with a candidate label set, which consists of one ground-truth label and several false positive labels. Multi-instance partial-label learning (MIPL) is a learning paradigm to deal with such tasks and has achieved favorable performances. Existing MIPL approach follows the instance-space paradigm by assigning augmented candidate label sets of bags to each instance and aggregating bag-level labels from instance-level labels. However, this scheme may be suboptimal as global bag-level information is ignored and the predicted labels of bags are sensitive to predictions of negative instances. In this paper, we study an alternative scheme where a multi-instance bag is embedded into a single vector representation. Accordingly, an intuitive algorithm named DEMIPL, i.e., Disambiguated attention Embedding for Multi-Instance Partial-Label learning, is proposed. DEMIPL employs a disambiguation attention mechanism to aggregate a multi-instance bag into a single vector representation, followed by a momentum-based disambiguation strategy to identify the ground-truth label from the candidate label set. Furthermore, we introduce a real-world MIPL dataset for colorectal cancer classification. Experimental results on benchmark and real-world datasets validate the superiority of DEMIPL against other well-established MIPL and partial-label learning methods. Our code and datasets will be made publicly available.

翻译：在许多实际任务中，所关注的对象可表示为关联一个候选标签集的多实例包，该标签集包含一个真实标签和若干假阳性标签。多实例偏标记学习（MIPL）是处理此类任务的学习范式，并已取得良好性能。现有MIPL方法遵循实例空间范式，通过为每个实例分配增强的候选标签集，并从实例级标签聚合得到包级标签。然而，该方案可能并非最优，因为全局包级信息被忽略，且包的预测标签对负实例的预测敏感。本文研究了一种替代方案，即将多实例包嵌入为单一向量表示。据此，我们提出了一种直观算法DEMIPL（即用于多实例偏标记学习的消歧注意力嵌入）。DEMIPL采用消歧注意力机制将多实例包聚合为单一向量表示，随后通过基于动量的消歧策略从候选标签集中识别真实标签。此外，我们引入了一个用于结直肠癌分类的真实世界MIPL数据集。在基准数据集和真实世界数据集上的实验结果验证了DEMIPL相较于其他成熟MIPL和偏标记学习方法的优越性。我们的代码和数据集将公开发布。