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Found 6 papers, 6 papers with code
Deep Image Matting: A Comprehensive Survey
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.
Referring Image Matting
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.
Ranked #1 on
Referring Image Matting (RefMatte-RW100)
on RefMatte
Rethinking Portrait Matting with Privacy Preserving
P3M-10k consists of 10, 421 high resolution face-blurred portrait images along with high-quality alpha mattes, which enables us to systematically evaluate both trimap-free and trimap-based matting methods and obtain some useful findings about model generalization ability under the privacy preserving training (PPT) setting.
Ranked #1 on
Image Matting
on P3M-10k
Deep Automatic Natural Image Matting
To address the problem, a novel end-to-end matting network is proposed, which can predict a generalized trimap for any image of the above types as a unified semantic representation.
Ranked #2 on
Image Matting
on AIM-500
Privacy-Preserving Portrait Matting
We systematically evaluate both trimap-free and trimap-based matting methods on P3M-10k and find that existing matting methods show different generalization capabilities when following the Privacy-Preserving Training (PPT) setting, i. e., training on face-blurred images and testing on arbitrary images.
Ranked #3 on
Image Matting
on P3M-10k
Bridging Composite and Real: Towards End-to-end Deep Image Matting
Furthermore, we provide a benchmark containing 2, 000 high-resolution real-world animal images and 10, 000 portrait images along with their manually labeled alpha mattes to serve as a test bed for evaluating matting model's generalization ability on real-world images.
Ranked #2 on
Image Matting
on AM-2K
