BiGenCap: Joint Bitemporal Generative Modeling via Reciprocal Prediction for Remote Sensing Image Change Captioning

Remote sensing image change captioning (RSICC) requires interpreting semantic changes between temporally separated images while preserving scene context and cross-temporal relationships. Existing methods mainly construct change representations through direct comparison and interaction of bitemporal visual features. Beyond such pairwise comparison, large-scale collections of bitemporal images contain diverse visual semantics and cross-temporal dependencies. Modeling these distributions can provide a generative prior for interpreting specific image pairs. We propose BiGenCap, which learns a joint generative prior over bitemporal images at scale and instantiates it for each image pair through reciprocal generative prediction. Specifically, we pretrain a Diffusion Transformer (DiT) with flow matching on approximately 230K image pairs from 21 public data sources, assigning independent flow times to the two temporal streams to jointly model within-stream semantics and cross-temporal dependencies. During captioning, reciprocal generative prediction alternately perturbs one temporal feature stream while keeping its counterpart clean. We exploit the resulting predictions in two complementary ways: generative discrepancy fusion (GDF) derives prediction-based discrepancy cues from the final DiT responses, while reciprocal relation modeling (RRM) uses intermediate representations to model local cross-temporal relations and aggregate visual content from the temporal counterpart. BiGenCap achieves aggregate captioning scores $S_{\mathrm{avg}}$ of 82.55 and 94.17 on LEVIR-CC and WHU-CDC, respectively. Further analyses highlight the value of genuine bitemporal pairing and the complementarity of discrepancy and relational cues.