  This paper studies co-segmenting the common semantic object in a set of
images. Existing works either rely on carefully engineered networks to mine the
implicit semantic information in visual features or require extra data (i.e.,
classification labels) for training. In this paper, we leverage the contrastive
language-image pre-training framework (CLIP) for the task. With a backbone
segmentation network that independently processes each image from the set, we
introduce semantics from CLIP into the backbone features, refining them in a
coarse-to-fine manner with three key modules: i) an image set feature
correspondence module, encoding global consistent semantic information of the
image set; ii) a CLIP interaction module, using CLIP-mined common semantics of
the image set to refine the backbone feature; iii) a CLIP regularization
module, drawing CLIP towards this co-segmentation task, identifying the best
CLIP semantic and using it to regularize the backbone feature. Experiments on
four standard co-segmentation benchmark datasets show that the performance of
our method outperforms state-of-the-art methods.
