Diffusion-based synthetic expansion of pedestrian attribute training sets yields 1.1 to 3.3 point mA gains over the base PAR model on PA100k, PETAzs, and RAPzs.
A systematic review of generative adversarial net- works for traffic state prediction: overview, taxonomy, and future prospects,
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Enhancing Zero-Shot Pedestrian Attribute Recognition with Synthetic Data Generation: A Comparative Study with Image-To-Image Diffusion Models
Diffusion-based synthetic expansion of pedestrian attribute training sets yields 1.1 to 3.3 point mA gains over the base PAR model on PA100k, PETAzs, and RAPzs.