Continual Learning Framework for Long-Sequence Video Understanding with Memory-Aware Visual Representations
Keywords:
continual learning, video understanding, memory-augmented networks, long-sequence modeling, system architecture, fairness, edge computing, governanceAbstract
Continuous video streams generated by autonomous vehicles, surveillance networks, and interactive media platforms demand machine perception systems that sustain high performance over indefinitely long temporal horizons without catastrophic forgetting. Traditional architectures designed for short video clips struggle to reconcile memory efficiency with the preservation of long-range dependencies, while static representations fail to adapt to shifting visual statistics. This paper proposes a continual learning framework that integrates memory-aware visual representations for long-sequence video understanding. The framework treats memory as an architectural primitive rather than a passive storage buffer, coupling compressible episodic representations with dynamic parameter allocation strategies. We examine structural trade-offs among external memory banks, generative replay, and elastic weight consolidation in the context of streaming video. The discussion extends to system-level considerations including distributed training infrastructure, privacy-preserving federated adaptation across distributed cameras, energy-aware deployment on edge devices, and governance mechanisms for accountability in continuously evolving perception pipelines. Through a detailed analysis of architecture robustness, fairness under non-stationary class distributions, and policy implications for monitored public spaces, we argue that memory-awareness must be elevated from a component-level optimization to a first-class design principle. The paper articulates a holistic research agenda in which visual representations, learning dynamics, and operational infrastructure co-evolve to support trustworthy, sustainable continual video intelligence.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
