부산대학교 도서관

By demonstrating robustness in scenarios characterized by high-speed motion and extreme lighting changes, event cameras hold great potential for enhancing the perception reliability of autonomous driving systems. Because of its novelty and data sparsity, the progress of event-based algorithms is hindered by the scarcity of high-quality labeled datasets. In this letter, we propose CMESS (Cross-Modal learning for Event-based Semantic Segmentation), which eliminates the need for event labels by transferring the model from labeled image datasets (source domain) to unlabeled event datasets (target domain) via unsupervised domain adaptation (UDA). Compared to existing UDA methods that require hard alignment of visually consistent embeddings, our approach achieves soft alignment via cross-attention and then augments it with knowledge distillation to convey fine-grained source knowledge to the target domain. Additionally, we introduce an event-driven bidirectional self-labeling method to generate weakly supervised signals for event-only datasets. These designs facilitate cross-modal learning without requiring per-pixel paired frames or online reconstruction. Experimental results show that our method outperforms existing state-of-the-art methods in both UDA and supervised settings on common evaluation benchmarks, making it a universal framework for further unlabeled event-related visual tasks.