CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866917380550033408 |
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| author | Tang, Weidong Sun, Hanbin Li, Zihan Wang, Yikai Zhang, Feifan |
| author_facet | Tang, Weidong Sun, Hanbin Li, Zihan Wang, Yikai Zhang, Feifan |
| contents | Remote sensing change detection (CD) aims to identify where land-cover semantics change across time, but most existing methods still assume a fixed label space and therefore cannot answer arbitrary user-defined queries. Open-vocabulary change detection (OVCD) instead asks for the change mask of a queried concept. In the fully training-free setting, however, dense concept responses are difficult to compare directly across dates: appearance variation, weak cross-concept competition, and the spatial continuity of many land-cover categories often produce noisy, fragmented, and semantically unreliable change evidence. We propose Consistency-Regularized Open-Vocabulary Change Detection (CoRegOVCD), a training-free dense inference framework that reformulates concept-specific change as calibrated posterior discrepancy. Competitive Posterior Calibration (CPC) and the Semantic Posterior Delta (SPD) convert raw concept responses into competition-aware queried-concept posteriors and quantify their cross-temporal discrepancy, making semantic change evidence more comparable without explicit instance matching. Geometry-Token Consistency Gate (GeoGate) and Regional Consensus Discrepancy (RCD) further suppress unsupported responses and improve spatial coherence through geometry-aware structural verification and regional consensus. Across four benchmarks spanning building-oriented and multi-class settings, CoRegOVCD consistently improves over the strongest previous training-free baseline by 2.24 to 4.98 F1$_C$ points and reaches a six-class average of 47.50% F1$_C$ on SECOND. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02160 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection Tang, Weidong Sun, Hanbin Li, Zihan Wang, Yikai Zhang, Feifan Computer Vision and Pattern Recognition Remote sensing change detection (CD) aims to identify where land-cover semantics change across time, but most existing methods still assume a fixed label space and therefore cannot answer arbitrary user-defined queries. Open-vocabulary change detection (OVCD) instead asks for the change mask of a queried concept. In the fully training-free setting, however, dense concept responses are difficult to compare directly across dates: appearance variation, weak cross-concept competition, and the spatial continuity of many land-cover categories often produce noisy, fragmented, and semantically unreliable change evidence. We propose Consistency-Regularized Open-Vocabulary Change Detection (CoRegOVCD), a training-free dense inference framework that reformulates concept-specific change as calibrated posterior discrepancy. Competitive Posterior Calibration (CPC) and the Semantic Posterior Delta (SPD) convert raw concept responses into competition-aware queried-concept posteriors and quantify their cross-temporal discrepancy, making semantic change evidence more comparable without explicit instance matching. Geometry-Token Consistency Gate (GeoGate) and Regional Consensus Discrepancy (RCD) further suppress unsupported responses and improve spatial coherence through geometry-aware structural verification and regional consensus. Across four benchmarks spanning building-oriented and multi-class settings, CoRegOVCD consistently improves over the strongest previous training-free baseline by 2.24 to 4.98 F1$_C$ points and reaches a six-class average of 47.50% F1$_C$ on SECOND. |
| title | CoRegOVCD: Consistency-Regularized Open-Vocabulary Change Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.02160 |