用边缘帧辅助识别，提升边缘设备动作识别的泛化能力
MoExDA: Domain Adaptation for Edge-based Action Recognition
- 融合RGB与边缘帧进行轻量域适应
- 有效抑制静态偏差，计算开销更低
- 适合资源受限的边缘计算场景
现代动作识别模型存在静态偏差问题，导致泛化性能下降。本文提出MoExDA，一种基于RGB与边缘信息的轻量级域适应方法，通过引入边缘帧补充RGB帧信息，以缓解静态偏差。实验表明，该方法在降低计算成本的同时，显著提升了动作识别的鲁棒性，优于以往方法。
原文摘要 · Abstract (English)
Modern action recognition models suffer from static bias, leading to reduced generalization performance. In this paper, we propose MoExDA, a lightweight domain adaptation between RGB and edge information using edge frames in addition to RGB frames to counter the static bias issue. Experiments demonstrate that the proposed method effectively suppresses static bias with a lower computational cost, allowing for more robust action recognition than previous approaches.
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