Filling Before Advancing: Capability-Gap-Driven Post-Training for Scenario-Specialized Remote Sensing MLLMs
Remote sensing multimodal large language models (RS-MLLMs) have advanced aerial-image understanding, but scenario specialization remains constrained by scarce high-quality supervision and incomplete capability coverage. We propose filling before advancing (FBA), a capability-gap-driven post-training route that develops prerequisite capabilities before scenario-specific adaptation. For coastal harbor understanding, we construct CPRS (Coastal-Port Remote Sensing), a three-layer dataset covering RGB, SAR, PAN, and NIR imagery. FBA organizes supervision into RS semantic anchoring, multi-source bridging, and evidence-grounded scenario tuning. We also introduce HarborEval, an eight-track benchm...
| Authors | Yuheng Zong, Minghua Wang, Xin Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 July 2026 |
| Last updated at source | 15 October 2026 |
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