By demonstrating robustness in scenarios charac-
terized 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
work, 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 with-
out 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 frame-
work for further unlabeled event-related visual tasks.