基于提升方案的野外环境中情感相关面部动态的隐式解耦
Lifting Scheme-Based Implicit Disentanglement of Emotion-Related Facial Dynamics in the Wild
AI总结：
本文提出隐式面部动态解耦框架IFDD，通过将小波提升方案扩展为可学习框架，利用两阶段模块从全局上下文中隐式解耦情感相关动态信息，在野外数据集上实现更高识别精度与可比效率。
摘要：
野外动态面部表情识别（DFER）在识别情感相关表情时面临重大挑战，这些表情常在时间和空间上被情感无关表情和全局上下文所稀释。大多数先前的DFER方法直接利用耦合的时空表示，可能将弱相关特征与情感无关上下文偏差结合。部分方法强调动态信息，但遵循显式指导，易受无关运动影响。本文提出一种新颖的隐式面部动态解耦框架（IFDD）。通过将小波提升方案扩展为完全可学习的框架，IFDD以隐式方式从情感无关全局上下文中解耦情感相关动态信息，即无需显式操作和外部指导。解耦过程包含两个阶段。首先是帧间静态-动态分割模块（ISSM）用于粗略解耦估计，探索帧间相关性以动态生成内容感知的分割索引。利用这些索引将帧特征分为两组，一组具有更大全局相似性，另一组具有更多独特动态特征。第二阶段是基于提升的聚合-解耦模块（LADM）用于进一步细化。LADM首先通过更新器聚合ISSM的两组特征以获得细粒度全局上下文特征，然后通过预测器从全局上下文中解耦情感相关的面部动态特征。在野外数据集上的大量实验表明，IFDD以更高的识别精度和可比的效率超越了先前的有监督DFER方法。代码可在 https://github.com/CyberPegasus/IFDD 获取。
英文摘要：
In-the-wild dynamic facial expression recognition (DFER) encounters a significant challenge in recognizing emotion-related expressions, which are often temporally and spatially diluted by emotion-irrelevant expressions and global context. Most prior DFER methods directly utilize coupled spatiotemporal representations that may incorporate weakly relevant features with emotion-irrelevant context bias. Several DFER methods highlight dynamic information for DFER, but following explicit guidance that may be vulnerable to irrelevant motion. In this paper, we propose a novel Implicit Facial Dynamics Disentanglement framework (IFDD). Through expanding wavelet lifting scheme to fully learnable framework, IFDD disentangles emotion-related dynamic information from emotion-irrelevant global context in an implicit manner, i.e., without exploit operations and external guidance. The disentanglement process contains two stages. The first is Inter-frame Static-dynamic Splitting Module (ISSM) for rough disentanglement estimation, which explores inter-frame correlation to generate content-aware splitting indexes on-the-fly. We utilize these indexes to split frame features into two groups, one with greater global similarity, and the other with more unique dynamic features. The second stage is Lifting-based Aggregation-Disentanglement Module (LADM) for further refinement. LADM first aggregates two groups of features from ISSM to obtain fine-grained global context features by an updater, and then disentangles emotion-related facial dynamic features from the global context by a predictor. Extensive experiments on in-the-wild datasets have demonstrated that IFDD outperforms prior supervised DFER methods with higher recognition accuracy and comparable efficiency. Code is available at https://github.com/CyberPegasus/IFDD.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
