从室内到户外：无监督域自适应步态识别
From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition
AI总结：
针对现有步态识别方法难以从室内场景迁移到户外场景的问题，提出基于不确定性估计与正则化的无监督域自适应步态识别方法，构建新基准并验证方法有效，将公开基准与代码。
摘要：
步态识别是一项重要的人工智能任务，随着深度学习的发展已取得快速进展。然而，现有基于学习的步态识别方法主要聚焦于单一域，尤其是受限的实验室环境。本文研究了无监督域自适应步态识别（UDA-GR）这一新问题，该任务利用室内场景（源域）的有监督标签学习步态识别器，并将其应用于户外野外场景（目标域）。为此，我们提出了一种基于不确定性估计与正则化的UDA-GR方法。具体而言，我们研究了室内与户外场景下的步态特征，用于估计步态样本的不确定性，并将其应用于目标域的无监督微调过程中，以缓解伪标签带来的噪声。我们还针对所提出的问题构建了全新基准，在该基准上的实验结果验证了所提方法的有效性。我们将公开本次工作的基准数据集与源代码。
英文摘要：
Gait recognition is an important AI task, which has been progressed rapidly with the development of deep learning. However, existing learning based gait recognition methods mainly focus on the single domain, especially the constrained laboratory environment. In this paper, we study a new problem of unsupervised domain adaptive gait recognition (UDA-GR), that learns a gait identifier with supervised labels from the indoor scenes (source domain), and is applied to the outdoor wild scenes (target domain). For this purpose, we develop an uncertainty estimation and regularization based UDA-GR method. Specifically, we investigate the characteristic of gaits in the indoor and outdoor scenes, for estimating the gait sample uncertainty, which is used in the unsupervised fine-tuning on the target domain to alleviate the noises of the pseudo labels. We also establish a new benchmark for the proposed problem, experimental results on which show the effectiveness of the proposed method. We will release the benchmark and source code in this work to the public.
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。
