S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
Remote sensing image change detection with transformers.IEEE Transactions on Geoscience and Remote Sensing, 60:1–14
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PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.
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Masks Can Talk: Extracting Structured Text Information from Single-Modal Images for Remote Sensing Change Detection
S2M extracts structured text quadruples from change masks to provide noise-free multimodal supervision, achieving 17.80% Sek and 66.14% F_scd on the new Gaza-Change-v2 dataset and outperforming LLM-based multimodal methods.
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UAV as Urban Construction Change Monitor: A New Benchmark and Change Captioning Model
PTNet is a prototype-guided task-adaptive model that jointly performs change detection and captioning on bi-temporal UAV imagery by modeling structured change semantics, outperforming prior methods on the new UCCD urban construction benchmark and WHU-CDC.