SPAM 2: Efficient Superpixel Anything using Joint Object Features

Recent superpixel methods based on foundation segmentation models have pushed accuracy further by constraining superpixels to lie within high-level objects. However, this gain comes with a high computational cost, which limits their use in practical or large-scale vision pipelines. In this work, we introduce SPAM 2 (SuperPixel Anything Model 2), an efficient object-aware superpixel framework that notably replaces the costly traditional iterative clustering by a fast trainable local pixel-to-seed assignment and leverages lightweight foundation segmentation models to constrain superpixels within objects. To further exploit high-level information, SPAM 2 fuses superpixel and object features through a joint representation before final assignments. We also introduce a new supervision strategy based directly on oracle superpixel maps to recover more regular regions from a denser training signal. Comprehensive experiments on standard benchmarks and a downstream superpixel-based task show that SPAM 2 achieves state-of-the-art superpixel accuracy, while its lightweight variant is an order of magnitude faster than other object-based methods, making it suitable for dedicated computer vision pipelines. Code and pretrained models will be made available.