Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

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 …

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 …

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 …

Generating accurate pseudo-labels under the supervision of image categories is a crucial
step in Weakly Supervised Semantic Segmentation (WSSS). In this work, we propose a Mat …

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 …

Copy-Paste is a simple and effective data augmentation strategy for instance segmentation.
By randomly pasting object instances onto new background images, it creates new training …

Low light enhancement has gained increasing importance with the rapid development of
visual creation and editing. However, most existing enhancement algorithms are designed …

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 …