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.

本文提出了一种基于图标签增强的新的多实例多标签分类学习框架（GLEMIML）以提高分类性能和利用标签的重要性。GLEMIML 首先通过建立图识别实例之间的相关性，然后通过非线性映射将从特征空间挖掘到的隐含信息迁移到标签空间，从而恢复标签的重要性，并通过匹配和交互机制训练增强的数据，GLEMIML (AvgRank:1.44) 在多个基准数据集上表现出比 SOTA 方法 (AvgRank:2.92) 更好的结果。