Uncertainty-guided diffusion inpainting augments semantic segmentation data by regenerating context around hard regions and training only on preserved original pixels, yielding mIoU gains on rare classes in Cityscapes, UAVID, and BDD100K.
Computer vision for autonomous vehicles: Prob- lems, datasets and state of the art.Foundations and Trends in Computer Graphics and Vision, 12(1-3):1–308, 2020
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Empirical benchmark finds YOLO26 superior on Pascal VOC accuracy and efficiency but YOLOv8 faster on GPU, with both models struggling similarly on VisDrone small-object detection.
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Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Uncertainty-guided diffusion inpainting augments semantic segmentation data by regenerating context around hard regions and training only on preserved original pixels, yielding mIoU gains on rare classes in Cityscapes, UAVID, and BDD100K.
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YOLO26 vs. YOLOv8: A Comprehensive Architectural Benchmark of Next-Generation Real-Time Object Detection Models
Empirical benchmark finds YOLO26 superior on Pascal VOC accuracy and efficiency but YOLOv8 faster on GPU, with both models struggling similarly on VisDrone small-object detection.