DynaGuide: A generalizable dynamic guidance framework for zero-shot guided unsupervised semantic segmentation

Zero-shot guided unsupervised image segmentation enables dense scene understanding without relying on target-domain annotations, making it particularly valuable in domains where labeled data is scarce. However, most existing approaches struggle to reconcile global semantic coherence with fine-grained boundary precision. This paper introduces DynaGuide, an adaptive segmentation framework that addresses this challenge through a novel dual-guidance strategy and dynamic loss optimization. Building on our prior work, DynaSeg, DynaGuide integrates global pseudo-labels with local boundary refinement via a lightweight CNN trained from scratch. Crucially, the global pseudo-labels can originate either from a fully unsupervised source, such as DiffSeg, or from a supervised-pretrained model such as SegFormer. In both cases, these models act only as frozen priors on unseen data, ensuring that DynaGuide itself trains entirely without ground-truth labels in the target domain. Training is driven by a multi-component loss that dynamically balances feature similarity, Huber-smoothed spatial continuity (including diagonal relationships), and semantic alignment with the global pseudo-labels. Extensive experiments on BSD500, PASCAL VOC2012, and COCO demonstrate that DynaGuide achieves state-of-the-art performance, improving mIoU by 17.5% on BSD500, 3.1% on PASCAL VOC2012, and 11.66% on COCO. With its modular design, strong generalization, and minimal computational footprint, DynaGuide offers a scalable and practical solution for zero-shot guided unsupervised segmentation in real-world settings. • Proposes DynaGuide: a dual-guidance framework for zero-shot unsupervised segmentation. • Combines static global pseudo-labels with dynamic local CNN refinement. • Introduces adaptive multi-loss: feature similarity, diagonal Huber continuity, and global guidance. • Trains fully label-free using DiffSeg or SegFormer pseudo-labels without fine-tuning. • Outperforms recent SOTA on BSD500, PASCAL VOC2012, and COCO with fewer parameters and FLOPs.