  In the realm of video object segmentation (VOS), the challenge of operating
under low-light conditions persists, resulting in notably degraded image
quality and compromised accuracy when comparing query and memory frames for
similarity computation. Event cameras, characterized by their high dynamic
range and ability to capture motion information of objects, offer promise in
enhancing object visibility and aiding VOS methods under such low-light
conditions. This paper introduces a pioneering framework tailored for low-light
VOS, leveraging event camera data to elevate segmentation accuracy. Our
approach hinges on two pivotal components: the Adaptive Cross-Modal Fusion
(ACMF) module, aimed at extracting pertinent features while fusing image and
event modalities to mitigate noise interference, and the Event-Guided Memory
Matching (EGMM) module, designed to rectify the issue of inaccurate matching
prevalent in low-light settings. Additionally, we present the creation of a
synthetic LLE-DAVIS dataset and the curation of a real-world LLE-VOS dataset,
encompassing frames and events. Experimental evaluations corroborate the
efficacy of our method across both datasets, affirming its effectiveness in
low-light scenarios.
