AI RESEARCH
SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units
arXiv CS.CV
•
ArXi:2604.10436v1 Announce Type: new Accurate semantic understanding of complex traffic signs-including those with intricate layouts, multi-lingual text, and composite symbols-is critical for autonomous driving safety. Current models, both specialized small ones and large Vision Language Models (VLMs), suffer from a significant bottleneck: a lack of compositional generalization, leading to failure when encountering novel sign configurations. To overcome this, we propose SignReasoner, a novel paradigm that transforms general VLMs into expert traffic sign reasoners.