Multi-instance multi-label (MIML) learning is widely applicated in numerous domains, such as the image classification where one image contains multiple instances correlated with multiple logic labels simultaneously. The related labels in existing MIML are all assumed as logical labels with equal significance. However, in practical applications in MIML, significance of each label for multiple instances per bag (such as an image) is significant different. Ignoring labeling significance will greatly lose the semantic information of the object, so that MIML is not applicable in complex scenes with a poor learning performance. To this end, this paper proposed a novel MIML framework based on graph label enhancement, namely GLEMIML, to improve the classification performance of MIML by leveraging label significance. GLEMIML first recognizes the correlations among instances by establishing the graph and then migrates the implicit information mined from the feature space to the label space via nonlinear mapping, thus recovering the label significance. Finally, GLEMIML is trained on the enhanced data through matching and interaction mechanisms. GLEMIML (AvgRank: 1.44) can effectively improve the performance of MIML by mining the label distribution mechanism and show better results than the SOTA method (AvgRank: 2.92) on multiple benchmark datasets.

翻译：多示例多标签（MIML）学习广泛应用于诸多领域，例如图像分类中，一幅图像包含多个示例，这些示例同时与多个逻辑标签相关联。现有MIML方法中的相关标签均被假定为具有同等重要性的逻辑标签。然而，在实际MIML应用中，每个包（如图像）内多个示例所对应的标签重要性存在显著差异。忽略标签重要性将极大损失对象的语义信息，导致MIML在复杂场景中适用性不佳且学习性能低下。为此，本文提出一种基于图标签增强的新型MIML框架，即GLEMIML，通过利用标签重要性提升MIML的分类性能。GLEMIML首先通过构建图来识别示例间的相关性，然后通过非线性映射将特征空间中挖掘的隐含信息迁移至标签空间，从而恢复标签重要性。最终，GLEMIML通过匹配与交互机制在增强数据上进行训练。GLEMIML（平均排名：1.44）通过挖掘标签分布机制有效提升了MIML的性能，并在多个基准数据集上取得了优于当前最优方法（平均排名：2.92）的结果。