In this paper, an active learning framework is proposed to make high quality trimap. There are two active learning methods which are employed.
There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC).
There are two active learning methods which are employed: minimization of uncertainty sampling (MUS) and maximization of expected model output change (EMOC).
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We propose an active matting method with recurrent reinforcement learning. The proposed framework involves human in the loop by sequentially detecting ...
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 ...
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The proposed framework involves human in the loop by sequentially detecting informative regions for trivial human judgement. Comparing to traditional matting ...
We adopt the reinforcement learning strategy to allow a direct supervision based on an arbitrary matting algorithm and a ground-truth alpha matte. The ...
In this paper, we introduce DiffusionMat, a novel image matting framework that employs a diffusion model for the transition from coarse to refined alpha mattes.
In this paper, we describe an active learning method that uses a committee of learners to reduce the number of training examples required for learning. Our ...
This page describes the work about matting. In this work we are also interested with the photometry of the image, not only with the geometry and so we examine ...