Segment and Matte Anything in a Unified Model

TL;DR

SAMA is a unified lightweight model extending SAM to perform both high-quality image segmentation and matting, achieving state-of-the-art results across multiple benchmarks with minimal additional parameters.

Contribution

The paper introduces SAMA, a novel unified model that combines segmentation and matting tasks within a single framework, enhancing accuracy and versatility.

Findings

Achieves state-of-the-art performance on segmentation benchmarks.

Effectively performs interactive image matting with minimal extra parameters.

Demonstrates strong adaptability across diverse datasets.

Abstract

Segment Anything (SAM) has recently pushed the boundaries of segmentation by demonstrating zero-shot generalization and flexible prompting after training on over one billion masks. Despite this, its mask prediction accuracy often falls short of the precision required in real-world applications. While several refinement modules have been proposed to boost SAM's segmentation quality, achieving highly accurate object delineation within a single, unified framework remains an open challenge. Furthermore, interactive image matting, which aims to generate fine-grained alpha mattes guided by diverse user hints, has not yet been explored in the context of SAM. Insights from recent studies highlight strong correlations between segmentation and matting, suggesting the feasibility of a unified model capable of both tasks. In this paper, we introduce Segment And Matte Anything (SAMA), a lightweight extension of SAM that delivers high-quality interactive image segmentation and matting with minimal extra parameters. Our Multi-View Localization Encoder (MVLE) captures detailed features from local views, while the Localization Adapter (Local-Adapter) refines mask outputs by recovering subtle boundary details. We also incorporate two prediction heads for each task into the architecture to generate segmentation and matting masks, simultaneously. Trained on a diverse dataset aggregated from publicly available sources, SAMA achieves state-of-the-art performance across multiple segmentation and matting benchmarks, showcasing its adaptability and effectiveness in a wide range of downstream tasks.