Spike cameras offer unique sensing capabilities but their sparse, asynchronous output challenges semantic understanding, especially for Spike Video-Language Alignment (Spike-VLA) where models like CLIP underperform due to modality mismatch. We introduce SPKLIP, the first architecture specifically for Spike-VLA. SPKLIP employs a hierarchical spike feature extractor that adaptively models multi-scale temporal dynamics in event streams, and uses spike-text contrastive learning to directly align spike video with language, enabling effective few-shot learning. A full-spiking visual encoder variant, integrating SNN components into our pipeline, demonstrates enhanced energy efficiency. Experiments show state-of-the-art performance on benchmark spike datasets and strong few-shot generalization on a newly contributed real-world dataset. SPKLIP's energy efficiency highlights its potential for neuromorphic deployment, advancing event-based multimodal research. The source code and dataset are available at [link removed for anonymity].
View on arXiv@article{gao2025_2505.12656,
title={ SPKLIP: Aligning Spike Video Streams with Natural Language },
author={ Yongchang Gao and Meiling Jin and Zhaofei Yu and Tiejun Huang and Guozhang Chen },
journal={arXiv preprint arXiv:2505.12656},
year={ 2025 }
}