基于语言引导和运动感知的通用识别姿态表示
Language-Guided and Motion-Aware Gait Representation for Generalizable Recognition
AI总结：
LMGait通过引入自然语言描述作为语义先验，结合运动感知模块和时间捕获模块，提升了姿态识别的通用性和准确性。
摘要：
姿态识别正在成为计算机视觉领域中一种有前景的技术和创新领域，具有在远程人类识别中的广泛应用。然而，现有方法通常依赖复杂的架构直接从图像中提取特征并应用池化操作以获得序列级表示。此类设计往往导致对静态噪声（例如服装）过拟合，同时无法有效捕捉动态运动区域，如手臂和腿部。这一瓶颈在存在类内变化的情况下尤为具有挑战性，其中同一人在不同环境条件下的姿态特征在特征空间中显著距离较大。为了解决上述挑战，我们提出了一种语言引导和运动感知的姿态识别框架，称为LMGait。据我们所知，LMGait是第一个将自然语言描述作为显式语义先验引入姿态识别任务的方法。特别是，我们利用设计的与姿态相关的语言提示来捕捉姿态序列中的关键运动特征。为了提高跨模态对齐，我们提出了运动感知模块（MAM），它通过自适应调整各种层次的语义信息来优化语言特征，以确保更好地与视觉表示对齐。此外，我们引入了运动时间捕获模块（MTCM）以增强姿态特征的辨别能力并提高模型的运动跟踪能力。我们进行了广泛的跨多个数据集的实验，结果证明了我们所提出网络的显著优势。具体来说，我们的模型在CCPG、SUSTech1K和CASIAB数据集上分别达到了88.5%、97.1%和97.5%的准确率，取得了最先进的性能。主页：https://dingwu1021.github.io/LMGait/
英文摘要：
Gait recognition is emerging as a promising technology and an innovative field within computer vision, with a wide range of applications in remote human identification. However, existing methods typically rely on complex architectures to directly extract features from images and apply pooling operations to obtain sequence-level representations. Such designs often lead to overfitting on static noise (e.g., clothing), while failing to effectively capture dynamic motion regions, such as the arms and legs. This bottleneck is particularly challenging in the presence of intra-class variation, where gait features of the same individual under different environmental conditions are significantly distant in the feature space. To address the above challenges, we present a Languageguided and Motion-aware gait recognition framework, named LMGait. To the best of our knowledge, LMGait is the first method to introduce natural language descriptions as explicit semantic priors into the gait recognition task. In particular, we utilize designed gait-related language cues to capture key motion features in gait sequences. To improve cross-modal alignment, we propose the Motion Awareness Module (MAM), which refines the language features by adaptively adjusting various levels of semantic information to ensure better alignment with the visual representations. Furthermore, we introduce the Motion Temporal Capture Module (MTCM) to enhance the discriminative capability of gait features and improve the model's motion tracking ability. We conducted extensive experiments across multiple datasets, and the results demonstrate the significant advantages of our proposed network. Specifically, our model achieved accuracies of 88.5%, 97.1%, and 97.5% on the CCPG, SUSTech1K, and CASIAB datasets, respectively, achieving state-of-the-art performance. Homepage: https://dingwu1021.github.io/LMGait/
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。
