Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras
Yuhui Lin, Jiahao Zhang, Siyuan Li, Jimin Xiao, Ding Xu, Wenjun Wu,, Jiaxuan Lu

TL;DR
This paper introduces USKT, a U-shaped state space model for event-to-RGB knowledge transfer, enabling effective use of event camera data with minimal tuning and improved performance on multiple datasets.
Contribution
The paper proposes a novel USKT framework with a bidirectional reverse state space model for efficient event-to-RGB knowledge transfer and resource conservation.
Findings
USKT improves model accuracy on multiple datasets.
The bidirectional reverse state space model reduces computational costs.
USKT demonstrates adaptability across different tasks.
Abstract
Event cameras, as an emerging imaging technology, offer distinct advantages over traditional RGB cameras, including reduced energy consumption and higher frame rates. However, the limited quantity of available event data presents a significant challenge, hindering their broader development. To alleviate this issue, we introduce a tailored U-shaped State Space Model Knowledge Transfer (USKT) framework for Event-to-RGB knowledge transfer. This framework generates inputs compatible with RGB frames, enabling event data to effectively reuse pre-trained RGB models and achieve competitive performance with minimal parameter tuning. Within the USKT architecture, we also propose a bidirectional reverse state space model. Unlike conventional bidirectional scanning mechanisms, the proposed Bidirectional Reverse State Space Model (BiR-SSM) leverages a shared weight strategy, which facilitates…
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Taxonomy
TopicsTechnology and Data Analysis · Innovation in Digital Healthcare Systems · Educational Systems and Policies
