{"ID":2840881,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12410","arxiv_id":"2511.12410","title":"Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection","abstract":"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","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2511.12410","url_pdf":"https://arxiv.org/pdf/2511.12410v1","authors":"[\"Xi Xiao\",\"Zhuxuanzi Wang\",\"Mingqiao Mo\",\"Chen Liu\",\"Chenrui Ma\",\"Yanshu Li\",\"Smita Krishnaswamy\",\"Xiao Wang\",\"Tianyang Wang\"]","published":"2025-11-16T01:28:45Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":607010,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2840881,"paper_url":"https://arxiv.org/abs/2511.12410","paper_title":"Self-Supervised Visual Prompting for Cross-Domain Road Damage Detection","repo_url":"https://github.com/xixiaouab/PROBE","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}