基于选择性强增强骨骼序列的自监督步态情绪表征学习
Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences
AI总结：
针对步态情绪识别标注数据不足及现有对比学习表征不充分的问题，本文提出结合选择性强增强、CFFN和分布散度最小化损失的自监督框架，并在E-Gait与Emilya上取得最优性能。
摘要：
情绪识别是情感计算的重要组成部分。从人类步态中提取情绪线索具有自然交互、非侵入性和远程检测等优势。近年来，自监督学习技术的引入为基于步态的情绪识别领域中因标注数据稀缺而产生的问题提供了一种切实可行的解决方案。然而，由于步态多样性有限以及骨骼特征表征不完整，现有对比学习方法在获取步态情绪方面通常效率较低。本文提出一种采用选择性强增强（SSA）的对比学习框架，用于自监督步态情绪表征，旨在从有限的标注步态数据中获得有效表征。首先，我们针对步态情绪识别任务提出一种SSA方法，包括上半身抖动和随机时空掩码。SSA的目标是生成更多样且更具针对性的正样本，并促使模型学习更具区分度和鲁棒性的特征表征。随后，我们设计了互补特征融合网络（CFFN），以促进跨域信息整合，从而获取拓扑结构特征和全局自适应特征。最后，我们采用分布散度最小化损失来监督普通增强查询和强增强查询的表征学习。我们的方法在Emotion-Gait（E-Gait）和Emilya数据集上进行了验证，并在不同评估协议下优于现有最先进方法。
英文摘要：
Emotion recognition is an important part of affective computing. Extracting emotional cues from human gaits yields benefits such as natural interaction, a nonintrusive nature, and remote detection. Recently, the introduction of self-supervised learning techniques offers a practical solution to the issues arising from the scarcity of labeled data in the field of gait-based emotion recognition. However, due to the limited diversity of gaits and the incompleteness of feature representations for skeletons, the existing contrastive learning methods are usually inefficient for the acquisition of gait emotions. In this paper, we propose a contrastive learning framework utilizing selective strong augmentation (SSA) for self-supervised gait-based emotion representation, which aims to derive effective representations from limited labeled gait data. First, we propose an SSA method for the gait emotion recognition task, which includes upper body jitter and random spatiotemporal mask. The goal of SSA is to generate more diverse and targeted positive samples and prompt the model to learn more distinctive and robust feature representations. Then, we design a complementary feature fusion network (CFFN) that facilitates the integration of cross-domain information to acquire topological structural and global adaptive features. Finally, we implement the distributional divergence minimization loss to supervise the representation learning of the generally and strongly augmented queries. Our approach is validated on the Emotion-Gait (E-Gait) and Emilya datasets and outperforms the state-of-the-art methods under different evaluation protocols.
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。
