SARVLM is the first vision-language foundation model for SAR, trained via domain transfer on a 1M image-text dataset and outperforming prior models on 13 benchmarks for retrieval, recognition, detection, and captioning.
Sarlang-1m: A benchmark for vision-language modeling in sar image understanding
3 Pith papers cite this work. Polarity classification is still indexing.
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Sentinel2Cap provides human-annotated captions for multimodal Sentinel satellite images, with zero-shot tests showing RGB outperforming SAR and prompts helping performance.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.