改进Transformer模型，提升传感器数据动作识别准确率
Detecting Informative Channels: ActionFormer
- 采用序列注意力机制与Swish激活函数，优化时序特征捕捉
- 在WEAR数据集上惯性数据mAP提升16.01%
- 适合做可穿戴设备动作识别的轻量化模型研究者
基于Transformer的动作识别（HAR）近年取得进展。ActionFormer通过输出活动边界和标签，提供了新视角。该方法原用于图像/视频输入，现被拓展至传感器信号输入。我们分析了深度学习架构，发现高时间动态性限制了模型对细微变化的捕捉能力，且空间与时间特征存在强依赖关系。为此提出改进版ActionFormer，通过仿照序列-激励策略减少额外参数，并选用Swish激活函数保留负向信息。在WEAR数据集上的实验表明，该方法在惯性数据上平均mAP提升16.01%。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) has recently witnessed advancements with Transformer-based models. Especially, ActionFormer shows us a new perspectives for HAR in the sense that this approach gives us additional outputs which detect the border of the activities as well as the activity labels. ActionFormer was originally proposed with its input as image/video. However, this was converted to with its input as sensor signals as well. We analyze this extensively in terms of deep learning architectures. Based on the report of high temporal dynamics which limits the model's ability to capture subtle changes effectively and of the interdependencies between the spatial and temporal features. We propose the modified ActionFormer which will decrease these defects for sensor signals. The key to our approach lies in accordance with the Sequence-and-Excitation strategy to minimize the increase in additional parameters and opt for the swish activation function to retain the information about direction in the negative range. Experiments on the WEAR dataset show that our method achieves substantial improvement of a 16.01\% in terms of average mAP for inertial data.
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