BiRNeT: A Unified Bidirectional Reasoning Paradigm for Open-Vocabulary Remote Sensing Change Detection

Open-vocabulary change detection (OVCD) aims to identify changes of arbitrary categories based on text descriptions, overcoming the reliance of conventional methods on predefined categories. However, existing approaches generally treat change localization and semantic recognition as separate processes, and the lack of interaction between them has become a key bottleneck for further performance improvement. In this paper, we propose BiRNet, a unified bidirectional reasoning paradigm. It uses change probabilities to constrain bi-temporal semantic information and performs joint inference to achieve unified prediction of change states and change categories. To support this paradigm, an Adaptive Multi-scale Context-Difference Fusion Module (AMCDF) is designed to enhance change representation. A Bidirectional Change-Constrained Semantic Transition Module (BCCST) is further proposed to couple localization and recognition into a joint decision through a semantic transition matrix constrained by change probabilities. BCCST requires no additional training and offers plug-and-play capability. Experimental results on four benchmark datasets demonstrate that BiRNet achieves competitive performance, with significant F1 gains of 23.69% and 6.93% on S2Looking and SECOND, respectively. The code will be released upon publication.