Differentiating a target foreground subject from its background is a fundamental computer vision task with widespread applications in image editing and composition. Basic segmentation approaches that use a binary pixel classification scheme do not consider the varying opacity in foreground/background edge pixels, resulting in hard and unnaturally contrastive edges around the foreground subject. Although recent deep learning-based natural image matting techniques have been shown to significantly improve fine-grained detail in these areas by estimating per-pixel opacity of the target foreground, these techniques rely on user-supplied trimaps as an auxiliary input, which limits their real-world applicability. In the new paper PP-Matting: High-Accuracy Natural Image Matting, a Baidu research team proposes PP-Matting, a trimap-free architecture that combines a high-resolution detail branch and a semantic context branch to achieve state-of-the-art performance on natural image matting tasks. In an input image comprising a target foreground subject and a background, the colour of each pixel is formulated as a linear combination equation of foreground and background colours, while an alpha matte defines the pixels' relative opacity.