Multi-label Learning on Image data has been widely exploited with deep learning models. However, supervised training on deep CNN models often cannot discover sufficient discriminative features for classification. As a result, numerous self-supervision methods are proposed to learn more robust image representations. However, most self-supervised approaches focus on single-instance single-label data and fall short on more complex images with multiple objects. Therefore, we propose an Object-Aware Self-Supervision (OASS) method to obtain more fine-grained representations for multi-label learning, dynamically generating auxiliary tasks based on object locations. Secondly, the robust representation learned by OASS can be leveraged to efficiently generate Class-Specific Instances (CSI) in a proposal-free fashion to better guide multi-label supervision signal transfer to instances. Extensive experiments on the VOC2012 dataset for multi-label classification demonstrate the effectiveness of the proposed method against the state-of-the-art counterparts.
翻译:以深层学习模式广泛利用了图像数据多标签学习,但是,关于深层CNN模型的监督培训往往无法发现足够的区分特征,因此,提议采用许多自我监督方法来学习更强有力的图像表现方式,然而,大多数自监督方法侧重于单一系统单一标签数据,而没有利用带有多个对象的更复杂的图像。因此,我们提议采用一个目标软件自检方法,以获得更多精细的分级演示,用于多标签学习,根据对象位置动态生成辅助任务。 其次,可以利用ASASS学习的强有力代表方式,以无提案的方式有效生成分类实例,以更好地指导多标签监督信号向实例的传输。关于多标签分类的VOC-2012数据集的广泛实验展示了拟议方法对最先进的对应方的有效性。