轻量时序卷积网络实现高效可解释的肢体情绪识别
Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks
- 用轻量时序卷积网络替代复杂图模型，提升效率
- 参数减少79.18%，推理延迟降低12.5倍，性能差距小于1.58%准确率
- 揭示上半身运动是关键线索，不同情绪依赖不同身体区域
基于肢体的姿态情绪识别对实时情感系统至关重要，但基于图的骨骼模型计算开销大。本文研究轻量级时序卷积网络（TCN）能否作为高效且可解释的替代方案。我们在DIEM-A数据集上评估多种TCN模型，并与基于图的时间序列图（G-TSG）基线对比，指标包括准确率、宏平均F1、参数量和推理延迟。尽管G-TSG表现最优，但TCN-Base在准确率上仅落后1.58个百分点、宏平均F1落后1.25点，同时参数量减少79.18%，分类器延迟降低约12.5倍。通过区域特异性TCN模型、零值遮蔽及G-TSG梯度显著性分析，发现上半身运动提供最强独立区域线索，各身体区域对不同情绪的贡献各异，且不同可解释方法揭示模型行为的不同侧面。结果表明，轻量级TCN可在保证性能的同时，为运动线索如何影响分类提供实用洞见。
原文摘要 · Abstract (English)
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.
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