Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918203617181696 |
|---|---|
| author | Xiao, Xi Wang, Zhuxuanzi Mo, Mingqiao Liu, Chen Ma, Chenrui Li, Yanshu Krishnaswamy, Smita Wang, Xiao Wang, Tianyang |
| author_facet | Xiao, Xi Wang, Zhuxuanzi Mo, Mingqiao Liu, Chen Ma, Chenrui Li, Yanshu Krishnaswamy, Smita Wang, Xiao Wang, Tianyang |
| contents | The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose \ours, a self-supervised framework that \emph{visually probes} target domains without labels. \ours introduces a Self-supervised Prompt Enhancement Module (SPEM), which derives defect-aware prompts from unlabeled target data to guide a frozen ViT backbone, and a Domain-Aware Prompt Alignment (DAPA) objective, which aligns prompt-conditioned source and target representations. Experiments on four challenging benchmarks show that \ours consistently outperforms strong supervised, self-supervised, and adaptation baselines, achieving robust zero-shot transfer, improved resilience to domain variations, and high data efficiency in few-shot adaptation. These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems. Source code is publicly available: https://github.com/xixiaouab/PROBE/tree/main |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection Xiao, Xi Wang, Zhuxuanzi Mo, Mingqiao Liu, Chen Ma, Chenrui Li, Yanshu Krishnaswamy, Smita Wang, Xiao Wang, Tianyang Computer Vision and Pattern Recognition The deployment of automated pavement defect detection is often hindered by poor cross-domain generalization. Supervised detectors achieve strong in-domain accuracy but require costly re-annotation for new environments, while standard self-supervised methods capture generic features and remain vulnerable to domain shift. We propose \ours, a self-supervised framework that \emph{visually probes} target domains without labels. \ours introduces a Self-supervised Prompt Enhancement Module (SPEM), which derives defect-aware prompts from unlabeled target data to guide a frozen ViT backbone, and a Domain-Aware Prompt Alignment (DAPA) objective, which aligns prompt-conditioned source and target representations. Experiments on four challenging benchmarks show that \ours consistently outperforms strong supervised, self-supervised, and adaptation baselines, achieving robust zero-shot transfer, improved resilience to domain variations, and high data efficiency in few-shot adaptation. These results highlight self-supervised prompting as a practical direction for building scalable and adaptive visual inspection systems. Source code is publicly available: https://github.com/xixiaouab/PROBE/tree/main |
| title | Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.12410 |