Object co-segmentation is a challenging task, which aims to segment common objects in multiple images at the same time. Generally, common information of the same object needs to be found to solve this problem. For various scenarios, common objects in different images only have the same semantic information. In this paper, we propose a deep object co-segmentation method based on channel and spatial attention, which combines the attention mechanism with a deep neural network to enhance the common semantic information. Siamese encoder and decoder structure are used for this task. Firstly, the encoder network is employed to extract low-level and high-level features of image pairs. Secondly, we introduce an improved attention mechanism in the channel and spatial domain to enhance the multi-level semantic features of common objects. Then, the decoder module accepts the enhanced feature maps and generates the masks of both images. Finally, we evaluate our approach on the commonly used datasets for the co-segmentation task. And the experimental results show that our approach achieves competitive performance.

对象共分割是一项具有挑战性的任务，旨在同时分割多个图像中的公共对象。通常，需要找到同一对象的公共信息来解决此问题。对于各种情况，不同图像中的公共对象仅具有相同的语义信息。在本文中，我们提出了一种基于通道和空间注意力的深度对象协同分割方法，该方法将注意力机制与深度神经网络相结合，以增强公共语义信息。连体编码器和解码器结构用于此任务。首先，使用编码器网络提取图像对的低级和高级特征。其次，我们在通道和空间域中引入了一种改进的注意力机制，以增强公共对象的多级语义特征。然后，解码器模块接受增强的特征图并生成两个图像的蒙版。最后，我们在共同细分任务的常用数据集上评估我们的方法。实验结果表明，我们的方法可以达到竞争性能。