LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter
For researchers comparing segmentation backbones, LUMA provides a fair evaluation framework and reveals that efficient token mixers are not efficient at high resolutions and pretraining matters more than architecture.
LUMA introduces a lightweight, backbone-agnostic mask-transformer head that enables fair benchmarking of segmentation backbones. Benchmarking 20 backbones reveals that plain ViT dominates the throughput Pareto-front and pretraining objective, not architecture, is the key factor for segmentation quality.
Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported differences rarely reflect the backbone itself. We introduce the Lightweight Universal Mask Adapter (LUMA), a lightweight, backbone-agnostic mask-transformer head that treats any backbone as a black-box feature extractor, letting a set of queries read from its features through cheap cross-attention. LUMA matches the accuracy of EoMT, the state-of-the-art efficient ViT-segmenter, at lower cost, while attaching unchanged to isotropic, hierarchical, convolutional, and mixture-of-experts backbones alike. Holding this head fixed, we benchmark 20 backbones, 11 pretraining schemes and a range of resolutions on ADE20K and Cityscapes under one modern recipe. We find that ``efficient'' token mixers fail to deliver efficiency even at the high resolutions that motivate them, with plain ViT holding the throughput Pareto-front at every resolution. Additionally, the pretraining objective, not the architecture, the lever the field has tuned hardest, governs segmentation quality.