Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training

Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation, demonstrating effectiveness in processing temporal information with energy efficiency and biological realism. Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics. Consequently, the voltage state at any time depends solely on its immediate past value, potentially limiting network expressiveness. Real neurons, however, exhibit complex dynamics influenced by long-term correlations and fractal dendritic structures, suggesting non-Markovian behavior. Motivated by this, we propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics. These fractional dynamics enable more expressive temporal patterns beyond the capability of integer-order models. For efficient training of fspikeDE, we introduce a gradient descent algorithm that optimizes parameters by solving an augmented fractional-order ODE (FDE) backward in time using adjoint sensitivity methods. Extensive experiments on diverse image and graph datasets demonstrate that fspikeDE consistently outperforms traditional SNNs, achieving superior accuracy, comparable energy efficiency, reduced training memory usage, and enhanced robustness against noise. Our approach provides a novel open-sourced computational toolbox for fractional-order SNNs, widely applicable to various real-world tasks.

NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results

This paper presents an overview of NTIRE 2025 the First Challenge on Event-Based Image Deblurring, detailing the proposed methodologies and corresponding results. The primary goal of the challenge is to design an event-based method that achieves high-quality image deblurring, with performance quantitatively assessed using Peak Signal-to-Noise Ratio (PSNR). Notably, there are no restrictions on computational complexity or model size. The task focuses on leveraging both events and images as inputs for single-image deblurring. A total of 199 participants registered, among whom 15 teams successfully submitted valid results, offering valuable insights into the current state of event-based image deblurring. We anticipate that this challenge will drive further advancements in event-based vision research.

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

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.