Adaptive Parameter-Efficient Fine-Tuning for Open-World Video Object Segmentation in Dynamic Scenes
Keywords:
parameter-efficient fine-tuning, open-world video object segmentation, dynamic scenes, adaptive systems, system design, robustness, governanceAbstract
The emergence of vision foundation models has radically transformed video object segmentation, yet their deployment in open-world dynamic scenes presents systemic challenges that extend far beyond model accuracy. This work systematically examines the design of adaptive parameter-efficient fine-tuning frameworks tailored to open-world video object segmentation under non-stationary conditions, emphasizing architectural decisions, infrastructure orchestration, robustness governance, and socio-technical policy dimensions. We argue that the current generation of parameter-efficient adapters, low-rank adaptors, and prompt-based tuning modules, while reducing the computational footprint of fine-tuning, lacks the architectural reflexivity needed to continuously reconfigure learnable parameters in response to distributional shifts, novel object categories, and resource-constrained edge environments. We propose a conceptual architecture in which a meta-controller monitors scene dynamics, uncertainty signals, and hardware telemetry to decide online which subsets of parameters should be updated, and at what granularity, without resorting to full-model retraining. Through this lens, the paper analyzes structural trade-offs involving latency, memory, energy consumption, and fairness, drawing on a broad body of evidence from video segmentation benchmarks, green AI studies, and algorithmic fairness research. Further, we explore the deployment ecosystems required to sustain such adaptive systems across geographies with heterogeneous regulatory regimes, addressing privacy, data sovereignty, and accountability gaps. The discussion extends to governance mechanisms that can audit adaptation trajectories and prevent discriminatory feedback loops in safety-critical applications. By synthesizing cross-domain insights from computer vision, systems engineering, and policy studies, the paper offers a roadmap for building robust, sustainable, and ethically-grounded segmentation infrastructures that can gracefully handle the unbounded variability of open-world dynamic scenes.
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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.
