Spiking Variational Graph Representation Inference for Video Summarization

Introduction

As short-form video content explodes on platforms like TikTok and YouTube, automated video summarization—distilling long videos into keyframes or short skims—has become a critical necessity. However, current methods struggle with three major hurdles: capturing long-term temporal dependencies, maintaining semantic coherence across the summary, and managing noise during multi-modal data fusion. Furthermore, many existing models are computationally heavy, limiting their deployment on edge devices.

To address these challenges, researchers have introduced SpiVG, a framework that combines brain-inspired Spiking Neural Networks (SNNs) with variational graph reasoning. By leveraging the event-driven nature of SNNs and the probabilistic rigor of variational inference, SpiVG creates efficient, query-aware video summaries with significantly reduced computational overhead.

The SpiVG Framework

The Spiking Variational Graph (SpiVG) network is designed to process video through a biologically inspired and mathematically robust pipeline. It consists of four primary modules:

1. Spiking Keyframe Extractor: Utilizing SNNs, the model identifies keyframes based on significant changes (spikes) like scene cuts or motion jumps, eliminating the need for manual thresholds.
2. Dynamic Aggregation Graph Reasoner (DAGR): This module constructs a graph where frames are nodes, using a Graph Adaptive Selector (GAS) to decouple object consistency from semantic flow.
3. Variational Inference Reconstruction Module (VIRM): To handle noise from multi-channel graph outputs, the model employs Evidence Lower Bound Optimization (ELBO) to reconstruct the "true" latent summary.
4. Text-oriented Multi-modal Embedding: Integrates user queries (e.g., via BERT) into a heterogeneous graph to ensure summaries align with specific user intent.

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Core Innovations and Logic

A key insight of SpiVG is the use of SNNs as natural selectors; the binary nature of spikes (0 or 1) perfectly matches the binary task of keyframe selection. Additionally, the model innovates in how it reasons about frame relationships. By separating the absolute value of frame similarity (contextual object consistency) from the sign of similarity (semantic viewpoint coherence), the model achieves finer-grained reasoning.

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By treating the final summary as a latent state to be reconstructed from noisy observations, the model becomes inherently more robust to information loss during deep network processing.

Experimental Results

The model was evaluated on major benchmarks including SumMe, TVSum, VideoXum, and the query-focused QFVS dataset. Key findings include:

• State-of-the-Art Performance: SpiVG achieved an F1 score of 53.7 on SumMe, outperforming previous leaders like SumGraph (51.4).
• Query Accuracy: It reached the highest average F1 score (50.20) on the QFVS dataset, demonstrating superior alignment with user instructions.
• Efficiency: The model requires only 191.9 million MACs—a nearly 20x improvement over models like CHAN (3755.6 million MACs). Its parameter footprint is a mere 2.97 MB.
• Rapid Convergence: Training visualizations indicate that the model reaches near-optimal solutions within just 20 epochs.

Conclusion

SpiVG represents a significant step forward in efficient video summarization. While it currently relies on pre-extracted features and faces the inherent training complexities of SNNs, its ability to provide high-quality, query-aware summaries with minimal computational cost makes it a promising candidate for real-world deployment on mobile and edge hardware. Future work will likely focus on end-to-end spiking architectures and explicit wall-clock latency testing on edge devices.