  Different from conventional image matting, which either requires user-defined
scribbles/trimap to extract a specific foreground object or directly extracts
all the foreground objects in the image indiscriminately, we introduce a new
task named Referring Image Matting (RIM) in this paper, which aims to extract
the meticulous alpha matte of the specific object that best matches the given
natural language description, thus enabling a more natural and simpler
instruction for image matting. First, we establish a large-scale challenging
dataset RefMatte by designing a comprehensive image composition and expression
generation engine to automatically produce high-quality images along with
diverse text attributes based on public datasets. RefMatte consists of 230
object categories, 47,500 images, 118,749 expression-region entities, and
474,996 expressions. Additionally, we construct a real-world test set with 100
high-resolution natural images and manually annotate complex phrases to
evaluate the out-of-domain generalization abilities of RIM methods.
Furthermore, we present a novel baseline method CLIPMat for RIM, including a
context-embedded prompt, a text-driven semantic pop-up, and a multi-level
details extractor. Extensive experiments on RefMatte in both keyword and
expression settings validate the superiority of CLIPMat over representative
methods. We hope this work could provide novel insights into image matting and
encourage more follow-up studies. The dataset, code and models are available at
https://github.com/JizhiziLi/RIM.
