Enhancing Audio-Visual Spiking Neural Networks through Semantic-Alignment and Cross-Modal Residual Learning
This work addresses the limitation of SNNs in real-world multimodal scenarios, offering an incremental improvement for researchers in neuromorphic computing and multimodal AI.
The paper tackles the problem of inefficient cross-modal information fusion in Spiking Neural Networks (SNNs) for audio-visual tasks by proposing a semantic-alignment cross-modal residual learning (S-CMRL) framework, achieving state-of-the-art performance on benchmark datasets like CREMA-D, UrbanSound8K-AV, and MNISTDVS-NTIDIGITS.
Humans interpret and perceive the world by integrating sensory information from multiple modalities, such as vision and hearing. Spiking Neural Networks (SNNs), as brain-inspired computational models, exhibit unique advantages in emulating the brain's information processing mechanisms. However, existing SNN models primarily focus on unimodal processing and lack efficient cross-modal information fusion, thereby limiting their effectiveness in real-world multimodal scenarios. To address this challenge, we propose a semantic-alignment cross-modal residual learning (S-CMRL) framework, a Transformer-based multimodal SNN architecture designed for effective audio-visual integration. S-CMRL leverages a spatiotemporal spiking attention mechanism to extract complementary features across modalities, and incorporates a cross-modal residual learning strategy to enhance feature integration. Additionally, a semantic alignment optimization mechanism is introduced to align cross-modal features within a shared semantic space, improving their consistency and complementarity. Extensive experiments on three benchmark datasets CREMA-D, UrbanSound8K-AV, and MNISTDVS-NTIDIGITS demonstrate that S-CMRL significantly outperforms existing multimodal SNN methods, achieving the state-of-the-art performance. The code is publicly available at https://github.com/Brain-Cog-Lab/S-CMRL.