属性一致的物体轮廓划分模型

该文提出一种基于全卷积深度残差网络、结合生成式对抗网络思想的基于属性一致的物体轮廓划分模型。采用物体轮廓划分网络作为生成器进行物体轮廓划分；该网络运用结构相似性作为区域划分的重构损失，从视觉系统的角度监督指导模型学习；使用全局和局部上下文判别网络作为双路判别器，对区域划分结果进行真伪判别的同时，结合对抗式损失提出一种联合损失用于监督模型的训练，使区域划分内容真实、自然且具有属性一致性。通过实例验证了该方法的实时性、有效性。

Object Contour Partition Model with Consistent Properties

A new object contour partition model based on the fully convolutional network, combined with the idea of generative counter network and consistent attributes is proposed. Firstly, the image region partition network is used as a generator to divide the image region. Then the structural similarity is used as the reconstruction loss of regional division to supervise and guide model learning from the perspective of visual system. Finally, the global and local context discrimination networks are used as double-path similarity to supervise the reconstruction loss of regional division and guide model learning from the discriminators to distinguish the truth and falsity of the results of regional division, and a joint loss is proposed to train the supervision model in combination with the adversarial loss, so as to make the content of regional division true, natural and with attribute consistency. The instantaneity and effectiveness of the method are verified by living examples.