AI RESEARCH
Contrastive-SDXL: Annotation-Preserving Night-Time Augmentation for Pedestrian Detection
arXiv CS.CV
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ArXi:2605.16406v1 Announce Type: new Night-time pedestrian detection remains challenging because labelled night-time data are limited and large illumination differences make daytime-only trained detectors unreliable. Latent diffusion models (LDMs) provide a powerful basis for image-to-image translation and cross-domain augmentation, but their effectiveness in safety-critical perception depends on whether detector-relevant objects and local semantic structure are preserved when translating between source and target domains.