用图神经网络与Transformer融合，提升步态情绪识别精度
CGTGait: Collaborative Graph and Transformer for Gait Emotion Recognition
- 结合图卷积与Transformer，捕捉步态的时空特征
- 在两个数据集上达到领先性能，推理计算量降低82.2%
- 适合关注行为理解与低资源部署的研究者
基于骨骼的步态情绪识别因其广泛应用受到广泛关注。然而，现有方法多聚焦于空间和局部时间运动信息的提取，难以捕捉长程时间依赖关系。本文提出一种新框架CGTGait，通过协同整合图卷积与Transformer，有效提取步态情绪识别所需的判别性时空特征。该框架由多个CGT模块组成，每个模块利用图卷积捕获帧级空间拓扑结构，同时通过Transformer建模全局时间依赖。此外，引入双向跨流融合（BCSF）模块，实现姿态与运动特征的高效融合，促进双流间互补信息交换。我们在Emotion-Gait和ELMD两个常用数据集上进行评估，结果表明CGTGait在保持或超越当前最优性能的同时，测试阶段计算复杂度降低约82.2%（仅需0.34G FLOPs）。代码已开源。
原文摘要 · Abstract (English)
Skeleton-based gait emotion recognition has received significant attention due to its wide-ranging applications. However, existing methods primarily focus on extracting spatial and local temporal motion information, failing to capture long-range temporal representations. In this paper, we propose \textbf{CGTGait}, a novel framework that collaboratively integrates graph convolution and transformers to extract discriminative spatiotemporal features for gait emotion recognition. Specifically, CGTGait consists of multiple CGT blocks, where each block employs graph convolution to capture frame-level spatial topology and the transformer to model global temporal dependencies. Additionally, we introduce a Bidirectional Cross-Stream Fusion (BCSF) module to effectively aggregate posture and motion spatiotemporal features, facilitating the exchange of complementary information between the two streams. We evaluate our method on two widely used datasets, Emotion-Gait and ELMD, demonstrating that our CGTGait achieves state-of-the-art or at least competitive performance while reducing computational complexity by approximately \textbf{82.2\%} (only requiring 0.34G FLOPs) during testing. Code is available at \small{https://github.com/githubzjj1/CGTGait.}
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