ARM: A Learnable, Plug-and-Play Module for CLIP-based Open-vocabulary Semantic Segmentation

TL;DR

This paper introduces ARM, a learnable module that enhances CLIP's internal features for open-vocabulary semantic segmentation, achieving better performance without extensive retraining or external models.

Contribution

ARM is a novel, lightweight, learnable module that adaptively refines CLIP features, enabling universal plug-and-play improvements for training-free OVSS frameworks.

Findings

Consistently improves baseline performance across multiple benchmarks.

Operates with negligible inference overhead.

Validates effectiveness as a universal post-processor.

Abstract

Open-vocabulary semantic segmentation (OVSS) is fundamentally hampered by the coarse, image-level representations of CLIP, which lack precise pixel-level details. Existing training-free methods attempt to resolve this by either importing priors from costly external foundation models (e.g., SAM, DINO) or by applying static, hand-crafted heuristics to CLIP's internal features. These approaches are either computationally expensive or sub-optimal. We propose the Attention Refinement Module (ARM), a lightweight, learnable module that effectively unlocks and refines CLIP's internal potential. Unlike static-fusion methods, ARM learns to adaptively fuse hierarchical features. It employs a semantically-guided cross-attention block, using robust deep features (K, V) to select and refine detail-rich shallow features (Q), followed by a self-attention block. The key innovation lies in a ``train once, use anywhere" paradigm. Trained once on a general-purpose dataset (e.g., COCO-Stuff), ARM acts as a universal plug-and-play post-processor for diverse training-free frameworks. Extensive experiments show that ARM consistently boosts baseline performance on multiple benchmarks with negligible inference overhead, establishing an efficient and effective paradigm for training-free OVSS.