In this article, we explore the challenge of integrating event data into Segment Anything Models (SAMs) to achieve robust and universal object segmentation in the event-centric domain. The key issue lies in aligning and calibrating embeddings from event data with those from RGB imagery. To tackle this, we leverage paired datasets of events and RGB images to extract valuable knowledge from the pre-trained SAM framework. Our approach involves a multi-scale feature distillation methodology that optimizes the alignment of token embeddings from event data with their RGB image counterparts, ultimately enhancing the overall architecture’s robustness. With a focus on calibrating pivotal token embeddings, we effectively manage differences in high-level embeddings between event and image domains. Extensive experiments on various datasets validate the effectiveness of our distillation method.
Readers interested in delving deeper can find the code for this methodology at http://codeurl.com.
Abstract: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 this http URL.