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SPKLIP: Aligning Spike Video Streams with Natural Language

Yongchang Gao, Meiling Jin, Zhaofei Yu, Tiejun Huang, Guozhang Chen

TL;DR

Spike cameras enable high-speed, high-dynamic-range sensing but yield sparse, asynchronous data that challenge semantic grounding with dense RGB models. SPKLIP introduces a spike-specific, end-to-end Spike-VLA architecture combining a Hierarchical Spike Feature Extractor, Spike-Text Contrastive Learning, and a Full-Spiking Visual Encoder to directly align spike streams with text. It achieves state-of-the-art performance on spike datasets, demonstrates strong few-shot generalization on a real-world spike dataset, and shows substantial energy savings with neuromorphic components. The work provides a foundational, energy-efficient framework for event-based multimodal perception and offers a released real-world spike dataset to accelerate research.

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].

SPKLIP: Aligning Spike Video Streams with Natural Language

TL;DR

Spike cameras enable high-speed, high-dynamic-range sensing but yield sparse, asynchronous data that challenge semantic grounding with dense RGB models. SPKLIP introduces a spike-specific, end-to-end Spike-VLA architecture combining a Hierarchical Spike Feature Extractor, Spike-Text Contrastive Learning, and a Full-Spiking Visual Encoder to directly align spike streams with text. It achieves state-of-the-art performance on spike datasets, demonstrates strong few-shot generalization on a real-world spike dataset, and shows substantial energy savings with neuromorphic components. The work provides a foundational, energy-efficient framework for event-based multimodal perception and offers a released real-world spike dataset to accelerate research.

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].
Paper Structure (24 sections, 13 equations, 5 figures, 4 tables)

This paper contains 24 sections, 13 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Spike camera
  5. Hierarchical Spike Feature Extractor (HSFE)
  6. Spatiotemporal Attentive Residual Network (STAR-Net)
  7. Spike-Text Contrastive Learning (STCL)
  8. Full-Spiking Visual Encoder (FSVE)
  9. Experiment
  10. Experimental Settings
  11. Datasets
  12. Implementation
  13. Comparative Analysis of Video-Clips and SPKLIP
  14. Ablation Study of Proposed Method
  15. Evaluate with Data from Real Shots
  16. ...and 9 more sections

Figures (5)

  • Figure 1: Illustration of the proposed end-to-end Spike-Based Video Understanding Framework (SPKLIP). This framework primarily consists of four key components: the Hierarchical Spike Feature Extractor (HSFE), the SpatioTemporal Attentive Residual Network (STAR-Net) module, a Text Encoder, and a Contrastive Learning Framework. Each component plays a critical role in enabling robust and efficient video understanding.
  • Figure 2: The HSFE module adaptively balances noise suppression and motion preservation via multi-scale temporal filtering and spatial attention. See text for details.
  • Figure 3: Architecture overview of FSVE. (a) Spiking ResNets extract spatial features with LIF neurons and TDBN. (b) E-SDSA module implements spike-driven attention with threshold normalization and sparse computation.
  • Figure 4: Performance Evaluation on Real Spike Camera Data: (A) 3D visualization of raw spike stream; (B) Processed video (wave); (C) Confusion matrix. Top-1 accuracy: 62.37% (2 shots), 80.81% (4 shots), 87.88% (6 shots), 90.41% (8 shots).
  • Figure A1: This figure displays three components, A: the first frame of the original RGB video from the UCF101 dataset, B: the spike lattices of the first five timesteps from the converted .dat file, C: the first frame of the reconstructed grayscale video generated through the TFI conversion process.