Intra-Class Consistency Guided Class-Agnostic Event Segmentation

Class-agnostic segmentation enables open-world object seg-mentation beyond predefined categories, and extending it to the eventdomain helps enhance perception in high-speed motion and extreme illu-mination scenarios. However, the sparsity and low redundancy of eventdata lead to weak intra-class consistency, making event-based segmen-tation typically underperform compared to RGB-based counterparts.To address this issue, we propose IC2 -ESeg, an Intra-class Consistencyguided, Class-agnostic Event Segmentation framework, including threemodules: intra-class consistency mining (ICM), lightweight informationinjection (LIJ), and cross-modal distillation (CMD). Grounded in theevent generation mechanism, the ICM module constructs noise, motion,and brightness representations by analyzing statistical noise frequency,spatio-temporal distribution, and brightness gradients, thereby enrichingthe informational content of event data. The LIJ module efficiently inte-grates these representations into the image encoder, while the CMD mod-ule transfers the general segmentation capability of Segment AnythingModel (SAM) to the event domain. Experiments on RGBE-SEG demon-strate that IC2 -ESeg achieves state-of-the-art (SOTA) performance inclass-agnostic segmentation. We also construct ComScene, a high reso-lution RGB-event segmentation dataset encompassing diverse scenarios.Results on MVSEC and ComScene demonstrate the strong generaliza-tion of IC2 -ESeg.