Toward Real-World High-Precision Image Matting and Segmentation

• URL: http://arxiv.org/abs/2601.12080v1
• Date: Sat, 17 Jan 2026 15:10:47 GMT
• Title: Toward Real-World High-Precision Image Matting and Segmentation
• Authors: Haipeng Zhou, Zhaohu Xing, Hongqiu Wang, Jun Ma, Ping Li, Lei Zhu,
• Abstract summary: We propose a Foreground Consistent Learning model, dubbed as FCLM, to address the aforementioned issues.<n>We first introduce a Depth-Aware Distillation strategy where we transfer the depth-related knowledge for better foreground representation.<n>To support interactive prediction, we contribute an Object-Oriented Decoder that can receive both visual and language prompts to predict the referring target.
• Score: 19.892441742183347
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: High-precision scene parsing tasks, including image matting and dichotomous segmentation, aim to accurately predict masks with extremely fine details (such as hair). Most existing methods focus on salient, single foreground objects. While interactive methods allow for target adjustment, their class-agnostic design restricts generalization across different categories. Furthermore, the scarcity of high-quality annotation has led to a reliance on inharmonious synthetic data, resulting in poor generalization to real-world scenarios. To this end, we propose a Foreground Consistent Learning model, dubbed as FCLM, to address the aforementioned issues. Specifically, we first introduce a Depth-Aware Distillation strategy where we transfer the depth-related knowledge for better foreground representation. Considering the data dilemma, we term the processing of synthetic data as domain adaptation problem where we propose a domain-invariant learning strategy to focus on foreground learning. To support interactive prediction, we contribute an Object-Oriented Decoder that can receive both visual and language prompts to predict the referring target. Experimental results show that our method quantitatively and qualitatively outperforms SOTA methods.

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