DynaGuide: A generalizable dynamic guidance framework for zero-shot guided unsupervised semantic segmentation
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it