用轻量适配器动态组合，实现嵌入式设备视频多标签分类能效提升。
Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices
- 按标签共现模式激活最少适配器，避免全模型切换。
- 在TAO数据集上能耗降40%，平均精度提升9点。
- 适合资源受限的实时视频分析场景，如智能摄像头。
嵌入式设备上的实时多标签视频分类受限于计算与能耗预算。视频流具有标签稀疏性、时序连续性和标签共现等结构特性，可被用于更高效的推理。我们提出Polymorph，一种上下文感知框架，每帧仅激活一组轻量级低秩适配器（LoRA）。每个适配器针对由共现模式导出的类别子集设计，并以共享主干网络的LoRA权重形式实现。运行时，Polymorph动态选择并组合所需适配器以覆盖活跃标签，避免全模型切换与权重合并。该模块化策略提升了可扩展性，同时降低延迟与能耗。在TAO数据集上，Polymorph相比强基线实现40%能耗降低，且mAP提升9点。代码开源：https://github.com/inference-serving/polymorph/
原文摘要 · Abstract (English)
Real-time multi-label video classification on embedded devices is constrained by limited compute and energy budgets. Yet, video streams exhibit structural properties such as label sparsity, temporal continuity, and label co-occurrence that can be leveraged for more efficient inference. We introduce Polymorph, a context-aware framework that activates a minimal set of lightweight Low Rank Adapters (LoRA) per frame. Each adapter specializes in a subset of classes derived from co-occurrence patterns and is implemented as a LoRA weight over a shared backbone. At runtime, Polymorph dynamically selects and composes only the adapters needed to cover the active labels, avoiding full-model switching and weight merging. This modular strategy improves scalability while reducing latency and energy overhead. Polymorph achieves 40% lower energy consumption and improves mAP by 9 points over strong baselines on the TAO dataset. Polymorph is open source at https://github.com/inference-serving/polymorph/.
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