2024年11月4日 · To address this limitation, we propose a novel zero-shot image matting model, called ZIM, with two key contributions: First, we develop a label converter that transforms segmentation labels into detailed matte labels, constructing the new SA1B-Matte dataset without costly manual annotations.

2024年11月1日 · We evaluate ZIM using the newly introduced MicroMat-3K test set, which contains high-quality micro-level matte labels. Experimental results show that ZIM outperforms existing methods in fine-grained mask generation and zero-shot generalization.

In this paper, we introduce a novel zero-shot image matting model. Recent models like SAM (Segment Anything Model) exhibit strong zero-shot capabilities, but they fall short in generating fine-grained, high-precision masks.

2024年11月1日 · To address this limitation, we propose a novel zero-shot image matting model, called ZIM, with two key contributions: First, we develop a label converter that transforms segmentation labels into detailed matte labels, constructing the new SA1B-Matte dataset without costly manual annotations.

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2012年3月10日 · ZIM (Zero-Shot Image Matting) is a groundbreaking model developed to set a new standard in precision matting while maintaining strong zero-shot capabilities. Like SAM, ZIM can generalize across diverse datasets and objects in a zero-shot paradigm.

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TL;DR 本研究针对现有分割基础模型在生成精确掩膜方面的不足，提出了一个新的零-shot图像抠图模型ZIM。关键创新在于开发了标签转换器来生成详细的掩膜标签，并通过构建新的SA1B-Matte数据集，使模型在无须昂贵人工标注的情况下能够生成精确掩膜。

2024年5月11日 · To address this limitation, we propose a novel zero-shot image matting model, called ZIM, with two key contributions: First, we develop a label converter that transforms segmentation labels into detailed matte labels, constructing the new SA1B-Matte dataset without costly manual annotations.

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