This work presents a deep object co-segmentation (DOCS) approach for
segmenting common objects of the same class within a pair of images. This means
that the method learns to ignore common, or uncommon, background stuff and
focuses on objects. If multiple object classes are presented in the image pair,
they are jointly extracted as foreground. To address this task, we propose a
CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level
semantic features of the foreground objects, a mutual correlation layer detects
the common objects, and finally, the decoder generates the output foreground
masks for each image. To train our model, we compile a large object
co-segmentation dataset consisting of image pairs from the PASCAL VOC dataset
with common objects masks. We evaluate our approach on commonly used datasets
for co-segmentation tasks and observe that our approach consistently
outperforms competing methods, for both seen and unseen object classes.

本文提出了一种基于深度学习的对象共分割方法，使用 CNN-based Siamese 编码器 - 解码器体系结构，利用相互关联的特征提取和掩码生成技术来较好地完成同类别对象的提取，实验结果表明，与其他算法相比，该方法具有更好的共分割效果。