基于轻量级时间卷积网络的高效且可解释的身体情感识别
Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks
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中文总结 AI 辅助
研究基于身体的情感识别，用轻量级时间卷积网络（TCN）替代计算昂贵的基于图的骨架模型。通过在DIEM - A上评估TCN模型并与G - TSG基线比较，发现TCN高效且能分析身体区域贡献，为情感识别提供新方法和见解。
中文摘要 AI 辅助
基于身体的情感识别对实时情感系统很重要，但基于图的骨架模型计算成本高。本文研究轻量级时间卷积网络（TCN）能否为基于身体的情感分类提供高效且可解释的替代方案。在DIEM - A上评估了一系列TCN模型，并与基于图的时间序列图（G - TSG）基线在准确率、宏F1、参数数量和推理延迟方面进行比较。结果表明，尽管G - TSG平均性能最高，但TCN - Base在参数少79.18%且分类器延迟降低约12.5倍的情况下，准确率和宏F1与G - TSG相差不大。还通过特定区域TCN模型等分析身体区域贡献，发现上半身运动提供最强独立区域线索，不同身体区域对不同情感有用性不同，不同解释方法捕捉模型行为不同方面。这些发现表明轻量级TCN可支持高效的基于身体的情感识别，并为运动线索对分类的贡献提供实际见解。
英文摘要
Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweight temporal convolutional networks (TCNs) can provide an efficient and interpretable alternative for body-based emotion classification. We evaluate a family of TCN models on DIEM-A and compare them with a graph-based time-series graph (G-TSG) baseline using accuracy, macro-F1, parameter count, and inference latency. Although G-TSG achieves the highest mean performance, TCN-Base remains within $1.58$ accuracy points and $1.25$ macro-F1 points while using $79.18\%$ fewer parameters and reducing classifier latency by approximately $12.5\times$. We also analyze body-region contributions using region-specific TCN models, zero-based occlusion, and G-TSG gradient saliency. The results show that upper-body motion provides the strongest standalone regional cue, that the usefulness of body regions varies across emotions, and that different interpretability methods capture distinct aspects of model behavior. These findings suggest that lightweight TCNs can support efficient body-based emotion recognition while also providing practical insight into how motion cues contribute to classification.
