Researchers have developed SC-Diff, a novel diffusion model designed for translating visible images into infrared representations. This framework enhances semantic consistency by calibrating self-attention mechanisms within the denoising network using semantic maps derived from a SAM3 model. The SC-Diff method adaptively adjusts attention biases based on category and attention dispersion, aiming to reduce cross-category interference while preserving global context. Experiments indicate that SC-Diff improves the quality of generated infrared images and yields more effective synthetic data for downstream tasks like infrared object detection. AI
IMPACT This research could improve synthetic data generation for infrared imaging, potentially benefiting applications in object detection and surveillance.
RANK_REASON This is a research paper detailing a new method for image translation. [lever_c_demoted from research: ic=1 ai=1.0]
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