Hierarchical Motion-Guided Video-Language Reasoning for Long-Horizon Human Activity Forecasting
Keywords:
hierarchical motion encoding, video-language reasoning, long-horizon forecasting, socio-technical systems, fairness, robustness, deployment infrastructureAbstract
This paper introduces a hierarchical motion-guided video-language reasoning framework designed for long-horizon human activity forecasting. The proposed architecture integrates multi-stream motion encoding with language-informed semantic reasoning to anticipate future actions over extended temporal horizons. Unlike conventional methods that treat video and text modalities independently, our framework leverages hierarchical motion abstraction at multiple temporal scales, enabling the model to capture both fine-grained kinematic cues and high-level behavioral patterns. In addition to technical design, we systematically examine structural trade-offs inherent in such systems, including computational scalability, data governance, and infrastructure deployment. We discuss the implications of deploying these models in socio-technical contexts such as autonomous driving, healthcare monitoring, and public surveillance, highlighting concerns around robustness, fairness, and accountability. Through cross-domain comparisons and illustrative case studies, we argue that long-horizon forecasting requires not only algorithmic advances but also careful consideration of policy and ethical constraints. The paper concludes with forward-looking recommendations for sustainable and equitable deployment of hierarchical video-language reasoning systems in real-world environments.
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