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 …

Neural networks have shown great abilities in estimating depth from a single image.
However, the inferred depth maps are well below one-megapixel resolution and often lack …

We present a systematic study on a new task called dichotomous image segmentation (DIS),
which aims to segment highly accurate objects from natural images. To this end, we …

In this paper, we propose a transformer-based image matting model called MatteFormer,
which takes full advantage of trimap information in the transformer block. Our method first …

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 …

Image matting is an inverse fusion process that separates the foreground and background
information by predicting alpha matte for each pixel. Recently, plain vision Transformers …

Given a composite image, image harmonization aims to adjust the foreground to make it
compatible with the background. High-resolution image harmonization is in high demand …

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 …

Existing natural image matting algorithms inevitably have flaws in their predictions on
difficult cases, and their one-step prediction manner cannot further correct these errors. In …

Natural image matting is a fundamental and challenging computer vision task. It has many
applications in image editing and composition. Recently, deep learning-based approaches …