AINeutralarXiv – CS AI · 10h ago6/10
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AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation
Researchers introduce AtteConDA, a novel approach to multi-condition image generation that resolves conflicts between simultaneous conditions (segmentation, depth, edges) to improve synthetic data quality for autonomous driving. The method enables more reliable data augmentation while preserving detailed scene structure, addressing critical data scarcity challenges in high-level driving task recognition.