Cross-Domain Video Object Segmentation via Memory-Guided Transfer Learning and Lightweight Adaptation
Keywords:
Video object segmentation; transfer learning; domain adaptation; memory-augmented networks; lightweight fine-tuning; edge computingAbstract
Video object segmentation is a critical capability in diverse domains ranging from autonomous driving and surgical robotics to environmental monitoring and augmented reality. Existing models achieve remarkable performance on curated benchmarks, yet they frequently degrade under domain shifts that arise from variations in illumination, camera viewpoint, object appearance, and scene context. The emergence of memory-augmented foundation architectures such as the Segment Anything Model series provides powerful spatiotemporal priors, but their vast parameter counts and memory footprints pose challenges for cross-domain adaptation in resource-constrained settings. This paper presents a systems-level examination of cross-domain video object segmentation through the lenses of memory-guided transfer learning and lightweight adaptation. We argue that effective deployment demands more than incremental algorithmic improvements; it requires a holistic rethinking of architecture selection, memory management, training regimes, and infrastructure orchestration. By analyzing the interplay between long-range temporal memory, parameter-efficient fine-tuning strategies, and edge-cloud resource partitioning, we identify fundamental trade-offs between segmentation fidelity, inference latency, energy consumption, and domain robustness. We further discuss governance considerations spanning fairness, data sovereignty, and sustainability, highlighting the societal responsibilities that accompany large-scale deployment of adaptive visual perception systems. Our analysis incorporates recent advances in memory-aware fine-tuning of video foundation models and situates them within a broader sociotechnical framework. We conclude by proposing evaluation protocols and policy guidelines that can steer the development of cross-domain video object segmentation toward equitable, efficient, and trustworthy outcomes.
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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.
