轻量级模型实现视频情感分析，兼顾速度与精度。
Lightweight Models for Emotional Analysis in Video
- 用MobileNetV4+多尺度3D MLP-Mixer提取时空特征
- 在ABAW 8竞赛上表现优异，适合实时应用
- 适合移动端和嵌入式设备部署
本研究提出一种高效时空特征提取方法，采用MobileNetV4作为主干网络，其通用逆瓶颈（UIB）结构可高效提取图像序列的分层特征表示，兼顾计算效率与语义丰富性。为捕捉时间依赖性，引入三级MLP-Mixer模块，在多分辨率下处理空间特征并保持结构完整性。在ABAW 8竞赛数据集上的实验表明，该方法在情感行为分析任务中表现良好。通过结合高效的视觉主干与结构化的时间建模机制，所提框架在计算效率与预测精度间取得平衡，适用于移动与嵌入式计算环境中的实时应用。
原文摘要 · Abstract (English)
In this study, we present an approach for efficient spatiotemporal feature extraction using MobileNetV4 and a multi-scale 3D MLP-Mixer-based temporal aggregation module. MobileNetV4, with its Universal Inverted Bottleneck (UIB) blocks, serves as the backbone for extracting hierarchical feature representations from input image sequences, ensuring both computational efficiency and rich semantic encoding. To capture temporal dependencies, we introduce a three-level MLP-Mixer module, which processes spatial features at multiple resolutions while maintaining structural integrity. Experimental results on the ABAW 8th competition demonstrate the effectiveness of our approach, showing promising performance in affective behavior analysis. By integrating an efficient vision backbone with a structured temporal modeling mechanism, the proposed framework achieves a balance between computational efficiency and predictive accuracy, making it well-suited for real-time applications in mobile and embedded computing environments.
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