In this paper we propose the Matting Anything Model (MAM) an efficient and versatile
framework for estimating the alpha matte of any instance in an image with flexible and …

Image matting refers to extracting precise alpha matte from natural images, and it plays a
critical role in various downstream applications, such as image editing. Despite being an ill …

Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the
trimap guidance. However, the production of trimap often requires significant labor, which …

Image matting has attracted growing interest in recent years for its wide applications in
numerous vision tasks. Most previous image matting methods rely on trimaps as auxiliary …

Mask-guided matting has shown great practicality compared to traditional trimap-based
methods. The mask-guided approach takes an easily-obtainable coarse mask as guidance …

This paper introduces the first two landmark pixel retrieval benchmarks. Like semantic
segmentation extends classification to the pixel level, pixel retrieval is an extension of image …

Different from conventional image matting, which either requires user-defined
scribbles/trimap to extract a specific foreground object or directly extracts all the foreground …

Image matting is a fundamental technique used to extract a fine foreground image from a
given image by estimating the opacity values of each pixel. It is one of the key techniques in …

This paper introduces an innovative approach for image matting that redefines the traditional
regression-based task as a generative modeling challenge. Our method harnesses the …

Although conventional matting formulation can separate foreground from background in
fractional occupancy which can be caused by highly transparent objects, complex …