PEARL: Geometry Aligns Semantics for Training-Free Open-Vocabulary Semantic Segmentation

TL;DR

PEARL is a training-free, efficient method for open-vocabulary semantic segmentation that aligns geometry between text and image features, improving accuracy without additional training or complex models.

Contribution

PEARL introduces a novel, training-free two-step inference method combining geometry alignment and Laplacian propagation for open-vocabulary segmentation.

Findings

Sets new state-of-the-art in training-free OVSS benchmarks.

Achieves superior performance with minimal latency and no extra data.

Operates effectively under both with-background and without-background protocols.

Abstract

Training-free open-vocabulary semantic segmentation (OVSS) promises rapid adaptation to new label sets without retraining. Yet, many methods rely on heavy post-processing or handle text and vision in isolation, leaving cross-modal geometry underutilized. Others introduce auxiliary vision backbones or multi-model pipelines, which increase complexity and latency while compromising design simplicity. We present PEARL, \textbf{\underline{P}}rocrust\textbf{\underline{e}}s \textbf{\underline{a}}lignment with text-awa\textbf{\underline{r}}e \textbf{\underline{L}}aplacian propagation, a compact two-step inference that follows an align-then-propagate principle. The Procrustes alignment step performs an orthogonal projection inside the last self-attention block, rotating keys toward the query subspace via a stable polar iteration. The text-aware Laplacian propagation then refines per-pixel logits on a small grid through a confidence-weighted, text-guided graph solve: text provides both a data-trust signal and neighbor gating, while image gradients preserve boundaries. In this work, our method is fully training-free, plug-and-play, and uses only fixed constants, adding minimal latency with a small per-head projection and a few conjugate-gradient steps. Our approach, PEARL, sets a new state-of-the-art in training-free OVSS without extra data or auxiliary backbones across standard benchmarks, achieving superior performance under both with-background and without-background protocols.