EvSAM: Segment Anything Model with Event-based Assistance

The general-purpose Segment Anything Model (SAM) is limited by the inherent constraints of RGB sensors, which render it inadequate for challenging real-world scenarios such as adverse lighting conditions and rapid motion. In contrast, event cameras, a novel type of bio-inspired visual sensor, offer distinct imaging advantages, including high temporal resolution and a high dynamic range. The event streams generated by these cameras provide spatiotemporal dynamic cues that are often absent in conventional image frames. To overcome the limitations of RGB-based models, we propose SAM with Event-based Assistance (EvSAM), a novel RGB-event multi-modal semantic segmentation framework. EvSAM leverages the strong generalization capabilities of SAM while incorporating the complementary characteristics of event data to enhance scene comprehension, particularly under adverse conditions. To address the challenges of fusing two modals (image and event) with large data format discrepancy, we introduce two core components: the Multi-spatiotemporal-scale Patch Alignment Block (MS²PAB) and the Event-based Feature Injector (EFInj) for SAM. Specifically, the MS^2PAB captures spatiotemporal semantic coherence from the event stream and transforms it into a frame-based complementary representation using a multi-spatiotemporal alignment strategy. The EFInj introduces a dynamic event feature update mechanism, wherein the fused features at a given layer guide the adaptive generation of deeper event representations. This process facilitates the integration of RGB spatial semantics with event-based motion cues. Owing to these core designs, EvSAM demonstrates superior performance on event-based semantic segmentation datasets, thereby fully validating its distinct advantages in handling extreme visual scenarios. Furthermore, we extend our model to the task of depth estimation, which further demonstrates its strong generalization ability and scalability for various downstream applications. © 2026 the owner/author(s). Publication rights licensed to ACM.