Memory-Augmented Multimodal Video Retrieval with Efficient Long-Term Visual Context Modeling
Keywords:
video retrieval, multimodal learning, memory augmentation, long-term context, efficient transformers, system architectureAbstract
The rapid growth of large-scale multimodal video archives demands retrieval systems capable of jointly reasoning about visual dynamics, associated textual descriptions, and long-range temporal dependencies. Existing video retrieval architectures often rely on dense spatiotemporal attention or compressed short-term representations, compromising either scalability or fidelity to extended events. This paper presents a memory-augmented framework that integrates an external hierarchical memory bank with a multimodal encoder-decoder structure to achieve efficient long-term visual context modeling. The design strategically separates transient working memory from persistent semantic storage, thereby reducing computational complexity while preserving the ability to reference distant video segments during retrieval. We examine structural trade-offs between memory granularity, retrieval latency, and representational completeness, and we situate these choices within the broader infrastructure requirements of modern content indexing pipelines. Through a system-level lens, we analyze deployment strategies spanning cloud, edge, and hybrid configurations, highlighting considerations of energy sustainability, fault tolerance, and fairness in retrieval outcomes. The paper further explores governance implications arising from persistent memory architectures that retain long-term visual evidence, addressing privacy erosion, auditability, and the potential for encoded biases to propagate across retrieval sessions. By synthesizing insights from computer vision, natural language processing, and distributed systems, we offer a holistic perspective on memory-augmented multimodal retrieval as a socio-technical infrastructure, pointing toward resilient, interpretable, and ethically grounded design pathways.
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