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Related Experiment Video

Updated: May 24, 2025

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Self-Supervised High-Order Information Bottleneck Learning of Spiking Neural Network for Robust Event-Based Optical

Shuangming Yang, Bernabe Linares-Barranco, Yuzhu Wu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces SeLHIB, a novel self-supervised learning algorithm for event cameras, enhancing optical flow estimation in noisy conditions. SeLHIB improves robustness and energy efficiency, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Neuromorphic Engineering
    • Machine Learning

    Background:

    • Event cameras excel in high-speed, high-dynamic-range visual perception.
    • Deep learning for event-based optical flow estimation needs better temporal feature capture.
    • Spiking Neural Networks (SNNs) offer potential for efficient sequential data processing but struggle with generalization and robustness.

    Purpose of the Study:

    • To introduce SeLHIB, a self-supervised learning algorithm for robust event-based optical flow estimation.
    • To leverage information bottleneck theory within SNNs for improved spatiotemporal feature extraction.
    • To enhance generalization and robustness in noisy visual scenes.

    Main Methods:

    • Developed a novel spike-based self-supervised learning algorithm, SeLHIB, utilizing information bottleneck theory.
    • Employed nonlinear and high-order mutual information for enhanced information extraction and redundancy reduction.
    • Trained and evaluated the algorithm using event-based camera inputs under various noise conditions.

    Main Results:

    • SeLHIB demonstrated significantly enhanced generalization and robustness in optical flow estimation across different noise levels.
    • Achieved substantial energy savings: 90.44% reduction compared to Analog Neural Network (ANN) counterparts and 45.70% compared to Spiking Neural Network (SNN) counterparts.
    • Outperformed ANN implementations with comparable sizes and architectures, showing lower AEE (MVSEC) by 33.78% and lower RSAT (ECD) by 5.96%, and RSAT (HQF) by 6.21%.

    Conclusions:

    • SeLHIB represents the first self-supervised information bottleneck learning strategy for SNNs in event-based vision.
    • The proposed method effectively addresses the limitations of current SNNs in generalization and robustness for optical flow tasks.
    • SeLHIB offers a promising direction for energy-efficient and robust visual perception systems using event cameras.