Recently, instance segmentation has made great progress with the rapid development of deep neural networks. However, there still exist two main challenges including discovering indistinguishable objects and modeling the relationship between instances. To deal with these difficulties, we propose a novel object mining framework for instance segmentation. In this framework, we first introduce the semantics perceiving subnetwork to capture pixels that may belong to an obvious instance from the bottom up. Then, we propose an object excavating mechanism to discover indistinguishable objects. In the mechanism, preliminary perceived semantics are regarded as original instances with classifications and locations, and then indistinguishable objects around these original instances are mined, which ensures that hard objects are fully excavated. Next, an instance purifying strategy is put forward to model the relationship between instances, which pulls the similar instances close and pushes away different instances to keep intra-instance similarity and inter-instance discrimination. In this manner, the same objects are combined as the one instance and different objects are distinguished as independent instances. Extensive experiments on the COCO dataset show that the proposed approach outperforms state-of-the-art methods, which validates the effectiveness of the proposed object mining framework.
翻译:最近,随着深度神经网络的快速发展,实例分割取得了巨大的进展。然而,仍然存在两个主要挑战,包括发现无法区分的物体和建模实例之间的关系。为了解决这些困难,我们提出了一种新的物体挖掘框架用于实例分割。在这个框架中,我们首先引入了语义感知子网络,从下向上捕捉可能属于一个明显实例的像素。然后,我们提出了物体挖掘机制来发现无法区分的物体。在这个机制中,初步感知的语义被视为具有分类和位置的原始实例,然后在这些原始实例周围挖掘无法区分的物体,以确保难处理的物体被完全挖掘。接下来,我们提出了一种实例净化策略来建模实例之间的关系,将相似实例拉近,将不同实例推开,使得实例内部具有相似度和实例间具有区分度。这样,相同的对象被组合为一个实例,不同的对象则被区分为独立的实例。在COCO数据集上进行的大量实验证明,所提出的方法优于现有的最先进方法,验证了所提出的物体挖掘框架的有效性。