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arXiv · 2507.14959

Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices

Saeid Ghafouri·Mohsen Fayyaz·Xiangchen Li·Deepu John·Bo Ji·Dimitrios Nikolopoulos·Hans Vandierendonck
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Abstract

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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Saeid Ghafouri, Mohsen Fayyaz, Xiangchen Li, Deepu John, Bo Ji, Dimitrios Nikolopoulos, Hans Vandierendonck. 2026-01-11. Polymorph: Energy-Efficient Multi-Label Classification for Video Streams on Embedded Devices. https://arxiv.org/abs/2507.14959

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