Efficient Spatiotemporal Token Compression for Large-Scale Multimodal Video Retrieval
Keywords:
multimodal video retrieval, spatiotemporal token compression, vision transformers, large-scale systems, retrieval infrastructure, fairness, sustainability, AI governanceAbstract
The rapid growth of multimodal video archives demands retrieval systems that can efficiently index and search across vast collections of video, audio, and textual metadata. Transformer-based architectures have become the backbone of such systems, converting raw media streams into sequences of high-dimensional embeddings or tokens. However, the quadratic complexity of attention mechanisms and the sheer number of tokens generated from spatiotemporal video features create formidable computational and storage bottlenecks. This paper presents a comprehensive system-level analysis of spatiotemporal token compression as a strategic intervention for large-scale multimodal video retrieval. We examine the landscape of token reduction techniques—including pruning, merging, and learned selection—and their integration into end-to-end retrieval pipelines. The discussion extends beyond algorithmic innovation to address infrastructure design choices, trade-offs between compression ratio and retrieval relevance, systems-wide robustness, and the governance challenges that emerge when retrieval results shape public information access. We further investigate energy consumption, lifecycle carbon costs, fairness in multimodal representations, and the policy implications of deploying compressed retrieval systems in ethically sensitive domains. By synthesizing insights from computer vision, natural language processing, systems engineering, and technology policy, the paper identifies critical structural tensions and offers a forward-looking perspective on building sustainable, equitable, and high-performance video retrieval systems. The analysis underscores that efficient token compression is not merely a computational optimization but a sociotechnical design choice with far-reaching consequences for data governance, accessibility, and public trust in AI-driven information retrieval.
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