SPKLIP: Aligning Spike Video Streams with Natural Language

2025-05-19

AI Summary

Aligning sparse, asynchronous event-camera spike videos with natural language remains challenging due to modality mismatch, limiting the performance of existing CLIP-style models. Method: We propose Spike-VLA—the first dedicated spike video–language alignment framework—featuring (i) a hierarchical spike feature extractor for multi-scale temporal modeling, (ii) a spike–text contrastive learning paradigm, and (iii) an energy-efficient all-spike visual encoder variant. Contribution/Results: Spike-VLA achieves state-of-the-art performance on mainstream spike datasets; demonstrates strong few-shot generalization on a newly constructed real-world spike-video benchmark; and significantly reduces computational energy consumption, enabling deployment on neuromorphic hardware.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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].

Problem

• Aligning sparse spike video streams with natural language for semantic understanding
• Addressing modality mismatch in Spike Video-Language Alignment (Spike-VLA) tasks
• Improving energy efficiency in spike-based vision-language models

Innovation

• Hierarchical spike feature extractor for multi-scale dynamics
• Spike-text contrastive learning for direct alignment
• Full-spiking visual encoder for energy efficiency