Although the weakly supervised learning can effectively avoid the tedious data annotating process of deep learning approaches, the performance is still in urgent need of enhancement. In this paper, we endeavor to mine a ubiquitous and fundamental knowledge—Relation, to boost several existing classification and localization models without changing the original structure. We first propose a universal relation exploring scheme to mine the relations among entities. This scheme can be specialized into different instantiations including object, superpixel and pixel relations to stimulating different learning models. We adopt the object relations on a few-shot classification model to concentrate on the dominant object, and to boost its discriminative capacity. The superpixel relation is utilized to improve the performance of the saliency object detection models. The sensitivity of the pixel relations to the uncertain regions makes it suitable for distinguishing the disputed area in saliency detection results. Our experiments demonstrate that all the three relation instantiations can significantly boost the performance of the state-of-the-art learning models and optimize the visual result.

尽管弱监督学习可以有效地避免深度学习方法中繁琐的数据注释过程，但仍然迫切需要增强其性能。在本文中，我们致力于挖掘普遍存在的基本知识-关系，以在不更改原始结构的情况下增强几个现有的分类和本地化模型。我们首先提出一种普遍关系探索方案，以挖掘实体之间的关系。该方案可以专门化为不同的实例，包括对象，超像素和像素关系，以激发不同的学习模型。我们在几次镜头分类模型中采用对象关系，以专注于优势对象，并增强其判别能力。超像素关系用于提高显着性对象检测模型的性能。像素关系对不确定区域的敏感性使其适合于在显着性检测结果中区分争议区域。