Learning to Change by Critique and Correction: A Synergistic Framework for Remote Sensing Change Detection and Captioning

Change Detection (CD) and Change Captioning (CC) are two core tasks for understanding land-cover evolution in remote sensing imagery. Existing approaches have explored CD-CC joint modeling through shared representations, task-specific decoding, feature interaction, and semantic guidance. However, the lack of explicit cross-task feedback mechanisms often leads to mutual interference, making it difficult to achieve both accurate detection and expressive descriptions. To address this issue, we propose the Learning to Change by Critique and Correction (LCCC) framework, which reformulates CD and CC as a critique-correction closed-loop process. In LCCC, CD and CC no longer passively share features but interact through bidirectional critique and correction: the CD task provides explicit spatial constraints for CC, and CC, in turn, supervises CD via a Text-Guided Critique Attention (TGCA) mechanism, establishing a synergistic relationship where both tasks act as critics and correctors. Furthermore, we design a Reciprocal Suppression and Enhancement (RSE) module to purify cross-task representations and propose a Key Complementary Feature Fusion (KCFF) mechanism to bridge the gap between high-level semantics and low-level visual features, ensuring a balance between task specialization and cross-task enhancement. Extensive experiments demonstrate that LCCC significantly outperforms existing methods in both detection accuracy and description quality, validating the effectiveness and generality of the proposed critique-correction paradigm for synergistic multi-task modeling. The code of the proposed method is available at https://github.com/Throb16/Lccc.