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 …

To address the big data challenges, serverless multi-party collaborative training has recently
attracted attention in the data mining community, since they can cut down the …

In today's landscape, smartphones have evolved into hubs for hosting a multitude of deep
learning models aimed at local execution. A key realization driving this work is the notable …

Animal pose estimation is challenging for existing image-based methods because of limited
training data and large intra-and inter-species variances. Motivated by the progress of visual …

The task of image matting is an active research area in computer vision, and various trimap-
free methods have been proposed to improve its performance. However, these methods do …

Automatic and interactive matting largely improve image matting by respectively alleviating
the need for auxiliary input and enabling object selection. Due to different settings on …

We introduce in-context matting a novel task setting of image matting. Given a reference
image of a certain foreground and guided priors such as points scribbles and masks in …

Deep learning methods have significantly advanced the performance of image matting.
However, dataset biases can mislead the matting models to biased behavior. In this paper …

In this work, we address the task of few-shot part segmentation, which aims to segment the
different parts of an unseen object using very few labeled examples. It is found that …