  In this paper, we delve into the nuanced challenge of tailoring the Segment
Anything Models (SAMs) for integration with event data, with the overarching
objective of attaining robust and universal object segmentation within the
event-centric domain. One pivotal issue at the heart of this endeavor is the
precise alignment and calibration of embeddings derived from event-centric data
such that they harmoniously coincide with those originating from RGB imagery.
Capitalizing on the vast repositories of datasets with paired events and RGB
images, our proposition is to harness and extrapolate the profound knowledge
encapsulated within the pre-trained SAM framework. As a cornerstone to
achieving this, we introduce a multi-scale feature distillation methodology.
This methodology rigorously optimizes the alignment of token embeddings
originating from event data with their RGB image counterparts, thereby
preserving and enhancing the robustness of the overall architecture.
Considering the distinct significance that token embeddings from intermediate
layers hold for higher-level embeddings, our strategy is centered on accurately
calibrating the pivotal token embeddings. This targeted calibration is aimed at
effectively managing the discrepancies in high-level embeddings originating
from both the event and image domains. Extensive experiments on different
datasets demonstrate the effectiveness of the proposed distillation method.
Code in http://github.com/happychenpipi/EventSAM.
