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<br> Remote sensing (RS) photographs from a number of modalities and platforms exhibit numerous details because of variations in sensor characteristics and imaging perspectives. Extensive experiments on numerous RS vision-language duties reveal that RingMo-Agent not only proves effective in both visual understanding and subtle analytical tasks, but in addition exhibits sturdy generalizability across completely different platforms and sensing modalities. Compared to VLMs with strong localization capabilities, our model, having undergone multiple rounds of advantageous-tuning, demonstrates considerably improved understanding across different modalities. The original pretrained weights battle to handle complex RS scenes, usually misidentifying a number of distinct objects as a single entity, making it inadequate for high quality-grained recognition tasks. Probably the most similar work to Ov-GSR is finish-to-end open-vocabulary HOI detection (Wang et al., 2022; Lei et al., 2024b), which concurrently recognizes actions in an image while detecting humans and <a href="https://vuf.minagricultura.gov.co/Lists/Informacin%20Servicios%20Web/DispForm.aspx?ID=11744889">Homepage</a>; objects. Metrics. We report both per-class and general detection efficiency using mAP@50. Metrics. We computed the accuracy for every query type in addition to the overall average accuracy. The most common function for raster-primarily based evaluation is the common metric on intersection over union (mIoU).<br>
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