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

arXiv: https://arxiv.org/abs/2509.12791 · HTML: https://mlsanity.com/paper/2509.12791/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 23 of 23)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Fast Segment Anything](https://mlsanity.com/paper/2306.12156/) (2023)
- [SelFlow: Self-Supervised Learning of Optical Flow](https://mlsanity.com/paper/1904.09117/) (2019)
- [Superpixel Sampling Networks](https://mlsanity.com/paper/1807.10174/) (2018)
- [SEEDS: Superpixels Extracted via Energy-Driven Sampling](https://mlsanity.com/paper/1309.3848/) (2013)
- [Superpixel Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/2003.12929/) (2020)
- [Superpixels: An Evaluation of the State-of-the-Art](https://mlsanity.com/paper/1612.01601/) (2016)
- [StructNeRF: Neural Radiance Fields for Indoor Scenes with Structural Hints](https://mlsanity.com/paper/2209.05277/) (2022)
- [SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection](https://mlsanity.com/paper/2207.07898/) (2022)
- [Learning Hierarchical Image Segmentation For Recognition and By Recognition](https://mlsanity.com/paper/2210.00314/) (2022)
- [Superpixel Hierarchy](https://mlsanity.com/paper/1605.06325/) (2016)
- [Adaptive Superpixel for Active Learning in Semantic Segmentation](https://mlsanity.com/paper/2303.16817/) (2023)
- [SPFormer: Enhancing Vision Transformer with Superpixel Representation](https://mlsanity.com/paper/2401.02931/) (2024)
- [Learning the Superpixel in a Non-iterative and Lifelong Manner](https://mlsanity.com/paper/2103.10681/) (2021)
- [Superpixel Segmentation Using Gaussian Mixture Model](https://mlsanity.com/paper/1612.08792/) (2016)
- [HERS Superpixels: Deep Affinity Learning for Hierarchical Entropy Rate Segmentation](https://mlsanity.com/paper/2106.03755/) (2021)
- [Superpixel-informed Implicit Neural Representation for Multi-Dimensional Data](https://mlsanity.com/paper/2411.11356/) (2024)
- [Evaluation Framework of Superpixel Methods with a Global Regularity Measure](https://mlsanity.com/paper/1903.07162/) (2019)
- [Learning Invariant Inter-pixel Correlations for Superpixel Generation](https://mlsanity.com/paper/2402.18201/) (2024)
- [SuperPatchMatch: an Algorithm for Robust Correspondences using Superpixel Patches](https://mlsanity.com/paper/1903.07169/) (2019)
- [Superpixel Segmentation: A Long-Lasting Ill-Posed Problem](https://mlsanity.com/paper/2411.06478/) (2024)

## Built on by (most cited first, 3 of 3)

- [Remote SAMsing: From Segment Anything to Segment Everything](https://mlsanity.com/paper/2605.00256/) (2026)
- [Computer Vision with a Superpixelation Camera](https://mlsanity.com/paper/2603.26900/) (2026)
- [H-SPAM: Hierarchical Superpixel Anything Model](https://mlsanity.com/paper/2604.11218/) (2026)

## Abstract (arXiv)

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.