EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction

• URL: http://arxiv.org/abs/2503.19721v3
• Date: Tue, 01 Apr 2025 02:49:17 GMT
• Title: EventMamba: Enhancing Spatio-Temporal Locality with State Space Models for Event-Based Video Reconstruction
• Authors: Chengjie Ge, Xueyang Fu, Peng He, Kunyu Wang, Chengzhi Cao, Zheng-Jun Zha,
• Abstract summary: EventMamba is a specialized model designed for event-based video reconstruction tasks.<n>We show that EventMamba markedly improves speed while delivering superior visual quality compared to Transformer-based methods.
• Score: 66.84997711357101
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Leveraging its robust linear global modeling capability, Mamba has notably excelled in computer vision. Despite its success, existing Mamba-based vision models have overlooked the nuances of event-driven tasks, especially in video reconstruction. Event-based video reconstruction (EBVR) demands spatial translation invariance and close attention to local event relationships in the spatio-temporal domain. Unfortunately, conventional Mamba algorithms apply static window partitions and standard reshape scanning methods, leading to significant losses in local connectivity. To overcome these limitations, we introduce EventMamba--a specialized model designed for EBVR tasks. EventMamba innovates by incorporating random window offset (RWO) in the spatial domain, moving away from the restrictive fixed partitioning. Additionally, it features a new consistent traversal serialization approach in the spatio-temporal domain, which maintains the proximity of adjacent events both spatially and temporally. These enhancements enable EventMamba to retain Mamba's robust modeling capabilities while significantly preserving the spatio-temporal locality of event data. Comprehensive testing on multiple datasets shows that EventMamba markedly enhances video reconstruction, drastically improving computation speed while delivering superior visual quality compared to Transformer-based methods.

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