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• CoRegOVCD improves over the strongest previous training-free baseline by 2.24 to 4.98 F1_C points across four benchmarks.result 95%
• CoRegOVCD reaches a six-class average of 47.50% F1_C on the SECOND benchmark.result 95%
• CoRegOVCD is a training-free dense inference framework for open-vocabulary change detection.method 90%
• The framework reformulates concept-specific change as calibrated posterior discrepancy.method 85%
• Competitive Posterior Calibration (CPC) converts raw concept responses into competition-aware queried-concept posteriors.method 85%
• Geometry-Token Consistency Gate (GeoGate) suppresses unsupported responses through geometry-aware structural verification.method 85%
• Most existing change detection methods assume a fixed label space and cannot answer arbitrary user-defined queries.limitation 80%
• In training-free open-vocabulary change detection, dense concept responses are difficult to compare directly across dates due to appearance variation, weak cross-concept competition, and spatial continuity issues.technical 80%

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