Long-Term Visual Memory Distillation for Few-Shot Video Instance Segmentation
Keywords:
Video Instance Segmentation, Few-Shot Learning, Visual Memory Distillation, Long-Term Memory, Edge Deployment, System RobustnessAbstract
The task of video instance segmentation demands simultaneous detection, tracking, and pixel-level segmentation of object instances across extended temporal windows. In few-shot regimes, where annotated video samples are extremely scarce, traditional per-frame or short-term matching strategies degrade sharply because they lack the capacity to consolidate appearance variations and occlusion patterns that unfold over long durations. This paper presents a system-level framework for long-term visual memory distillation that addresses this brittleness by transferring temporally persistent representational priors from a large, computationally expensive memory-augmented teacher model into an efficient student network suitable for inference under tight resource budgets. The architecture organizes visual memory along three interlocking tiers: a slow-updating global store that captures category-invariant appearance prototypes, a faster intermediate buffer that tracks instance-level dynamics across hundreds of frames, and a lightweight episodic cache that handles immediate motion associations. Distillation proceeds through a multi-objective procedure in which the student jointly learns to reproduce teacher-assigned instance embeddings, temporal affinity matrices, and future-frame feature predictions. The design is evaluated through the lens of infrastructural viability on edge accelerators, energy sustainability, fairness in domain transfer, and governance of large-scale video data. We analyze the trade-offs between memory capacity and latency, the implications of aggressive pruning on long-tail class performance, and the deployment strategies that enable robust few-shot generalization without violating privacy or consent norms. By framing the problem as one of institutionalized knowledge preservation across time, the paper articulates a pathway toward reliable, equitable, and environmentally conscious video understanding systems.
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