Noise2Map repurposes diffusion model denoising into a direct predictor for semantic segmentation and change detection tasks in remote sensing, achieving top average ranks on benchmark datasets.
Diffusionmodelissecretlyatraining-freeopenvocabularysemantic segmenter
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CAFe-DINO achieves SOTA open-vocabulary semantic segmentation on remote sensing datasets by leveraging DINOv3 features with cost aggregation and upsampling, fine-tuned solely on an RS-targeted COCO-Stuff subset.
FA-Seg delivers state-of-the-art training-free open-vocabulary segmentation performance (43.8% mIoU average) on standard benchmarks by extracting and refining attention from a single forward pass of a pretrained diffusion model.
citing papers explorer
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Noise2Map: End-to-End Diffusion Model for Semantic Segmentation and Change Detection
Noise2Map repurposes diffusion model denoising into a direct predictor for semantic segmentation and change detection tasks in remote sensing, achieving top average ranks on benchmark datasets.
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DINO Soars: DINOv3 for Open-Vocabulary Semantic Segmentation of Remote Sensing Imagery
CAFe-DINO achieves SOTA open-vocabulary semantic segmentation on remote sensing datasets by leveraging DINOv3 features with cost aggregation and upsampling, fine-tuned solely on an RS-targeted COCO-Stuff subset.
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FA-Seg: A Fast and Accurate Diffusion-Based Method for Open-Vocabulary Segmentation
FA-Seg delivers state-of-the-art training-free open-vocabulary segmentation performance (43.8% mIoU average) on standard benchmarks by extracting and refining attention from a single forward pass of a pretrained diffusion model.