Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning

• URL: http://arxiv.org/abs/2505.14535v1
• Date: Tue, 20 May 2025 15:55:11 GMT
• Title: Spiking Neural Networks with Temporal Attention-Guided Adaptive Fusion for imbalanced Multi-modal Learning
• Authors: Jiangrong Shen, Yulin Xie, Qi Xu, Gang Pan, Huajin Tang, Badong Chen,
• Abstract summary: We propose a temporal attention-guided adaptive fusion framework for multimodal spiking neural networks (SNNs)<n>The proposed framework implements adaptive fusion, especially in the temporal dimension, and alleviates the modality imbalance during multimodal learning.<n>The system resolves temporal misalignment through learnable time-warping operations and faster modality convergence coordination than baseline SNNs.
• Score: 32.60363000758323
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multimodal spiking neural networks (SNNs) hold significant potential for energy-efficient sensory processing but face critical challenges in modality imbalance and temporal misalignment. Current approaches suffer from uncoordinated convergence speeds across modalities and static fusion mechanisms that ignore time-varying cross-modal interactions. We propose the temporal attention-guided adaptive fusion framework for multimodal SNNs with two synergistic innovations: 1) The Temporal Attention-guided Adaptive Fusion (TAAF) module that dynamically assigns importance scores to fused spiking features at each timestep, enabling hierarchical integration of temporally heterogeneous spike-based features; 2) The temporal adaptive balanced fusion loss that modulates learning rates per modality based on the above attention scores, preventing dominant modalities from monopolizing optimization. The proposed framework implements adaptive fusion, especially in the temporal dimension, and alleviates the modality imbalance during multimodal learning, mimicking cortical multisensory integration principles. Evaluations on CREMA-D, AVE, and EAD datasets demonstrate state-of-the-art performance (77.55\%, 70.65\% and 97.5\%accuracy, respectively) with energy efficiency. The system resolves temporal misalignment through learnable time-warping operations and faster modality convergence coordination than baseline SNNs. This work establishes a new paradigm for temporally coherent multimodal learning in neuromorphic systems, bridging the gap between biological sensory processing and efficient machine intelligence.

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