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ISTASTrack: Bridging ANN and SNN via ISTA Adapter for RGB-Event Tracking.

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    Summary
    This summary is machine-generated.

    ISTASTrack introduces a novel hybrid artificial neural network (ANN) and spiking neural network (SNN) tracker for RGB-Event visual object tracking. This approach effectively fuses RGB and event stream data, achieving state-of-the-art performance and high energy efficiency.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Neuroscience

    Background:

    • RGB-Event tracking combines RGB images and event streams for enhanced visual object tracking.
    • Existing artificial neural networks (ANNs) face challenges in processing sparse, asynchronous event data.
    • Hybrid ANN-SNN architectures show promise but struggle with effective feature fusion across paradigms.

    Purpose of the Study:

    • To propose ISTASTrack, the first transformer-based ANN-SNN hybrid tracker for RGB-Event tracking.
    • To develop a method for effectively fusing features from heterogeneous ANN and SNN paradigms.
    • To improve the performance and energy efficiency of visual object tracking using RGB-Event data.

    Main Methods:

    • A two-branch model using a vision transformer for RGB data and a spiking transformer for event streams.
    • Introduction of ISTA (Iterative Shrinkage-Thresholding Algorithm) adapters for bidirectional feature interaction between ANN and SNN branches.
    • Incorporation of a temporal downsampling attention module for aligning multi-step SNN features with single-step ANN features.

    Main Results:

    • ISTASTrack achieved state-of-the-art performance on RGB-Event tracking benchmarks (FE240hz, VisEvent, COESOT, FELT).
    • The proposed hybrid approach demonstrated high energy efficiency.
    • Successful fusion of features across heterogeneous ANN and SNN paradigms was achieved.

    Conclusions:

    • Hybrid ANN-SNN designs are effective and practical for robust RGB-Event visual tracking.
    • ISTASTrack represents a significant advancement in leveraging complementary data sources for tracking.
    • The developed ISTA adapters effectively bridge modality and paradigm gaps in hybrid models.