Due to the expensive costs of collecting labels in multi-label classification datasets, partially annotated multi-label classification has become an emerging field in computer vision. One baseline approach to this task is to assume unobserved labels as negative labels, but this assumption induces label noise as a form of false negative. To understand the negative impact caused by false negative labels, we study how these labels affect the model's explanation. We observe that the explanation of two models, trained with full and partial labels each, highlights similar regions but with different scaling, where the latter tends to have lower attribution scores. Based on these findings, we propose to boost the attribution scores of the model trained with partial labels to make its explanation resemble that of the model trained with full labels. Even with the conceptually simple approach, the multi-label classification performance improves by a large margin in three different datasets on a single positive label setting and one on a large-scale partial label setting. Code is available at https://github.com/youngwk/BridgeGapExplanationPAMC.

翻译：由于多标签分类数据集标注成本高昂，部分标注的多标签分类已成为计算机视觉领域的新兴研究方向。针对此任务的一种基线方法是将未观测到的标签假定为负标签，但这一假设会以假负标签形式引入标签噪声。为理解假负标签造成的负面影响，我们研究了这些标签如何影响模型解释。我们观察到，分别使用完整标签和部分标签训练的两个模型，其解释突出区域相似但缩放尺度不同，后者往往具有较低归因得分。基于这些发现，我们提出增强部分标签训练模型的归因得分，使其解释更接近完整标签训练模型的解释。尽管该方法概念简单，但在三个不同数据集的单正标签设置及一个大规模部分标签设置中，多标签分类性能均有大幅提升。代码开源地址：https://github.com/youngwk/BridgeGapExplanationPAMC。