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.
Diffusiondet: Diffusion model for object detection,
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SD-ReID trains a ViT to extract identity and view conditions, fine-tunes Stable Diffusion to generate view-mimicking features, adds a View-Refined Decoder, and combines both identity and all-view features for retrieval on aerial-ground re-identification benchmarks.
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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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SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification
SD-ReID trains a ViT to extract identity and view conditions, fine-tunes Stable Diffusion to generate view-mimicking features, adds a View-Refined Decoder, and combines both identity and all-view features for retrieval on aerial-ground re-identification benchmarks.