Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation

• URL: http://arxiv.org/abs/2509.12791v1
• Date: Tue, 16 Sep 2025 08:09:24 GMT
• Title: Superpixel Anything: A general object-based framework for accurate yet regular superpixel segmentation
• Authors: Julien Walther, Rémi Giraud, Michaël Clément,
• Abstract summary: SPAM (SuperPixel Anything Model) is a versatile framework for segmenting images into accurate yet regular superpixels.<n>We leverage a large-scale pretrained model for semantic-agnostic segmentation to ensure that superpixels align with object masks.<n> SPAM can handle any prior high-level segmentation, resolving uncertainty regions, and is able to interactively focus on specific objects.
• Score: 1.121518046252855
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Superpixels are widely used in computer vision to simplify image representation and reduce computational complexity. While traditional methods rely on low-level features, deep learning-based approaches leverage high-level features but also tend to sacrifice regularity of superpixels to capture complex objects, leading to accurate but less interpretable segmentations. In this work, we introduce SPAM (SuperPixel Anything Model), a versatile framework for segmenting images into accurate yet regular superpixels. We train a model to extract image features for superpixel generation, and at inference, we leverage a large-scale pretrained model for semantic-agnostic segmentation to ensure that superpixels align with object masks. SPAM can handle any prior high-level segmentation, resolving uncertainty regions, and is able to interactively focus on specific objects. Comprehensive experiments demonstrate that SPAM qualitatively and quantitatively outperforms state-of-the-art methods on segmentation tasks, making it a valuable and robust tool for various applications. Code and pre-trained models are available here: https://github.com/waldo-j/spam.

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