注意你的朝向：步态识别中的视角偏置域差距与无监督适应
Watch Where You Head: A View-biased Domain Gap in Gait Recognition and Unsupervised Adaptation
AI总结：
该研究揭示步态识别UDA中由目标域视角或行走方向偏置导致的域差距，提出结合课程学习与三元组选择的GOUDA，并在多数据集和骨干网络上验证其优越性能。
摘要：
步态识别是一项旨在通过人们的行走模式来识别身份的计算机视觉任务。尽管现有方法通常在特定数据集上表现出高性能，但它们缺乏泛化到未见场景的能力。无监督域适应（UDA）试图将在源域上以监督方式预训练的模型适应到未标记的目标域。关于步态识别的UDA研究仅有少数工作，且只针对有限场景提出解决方案。在本文中，我们揭示了步态识别模型适应过程中的一个基本现象，该现象由目标域中对视角或行走方向的偏置所导致。随后，我们提出一种补救措施，通过结合课程学习的新颖三元组选择策略来减少这种偏置。为此，我们提出了基于步态朝向的无监督域适应方法（GOUDA）。我们在四个广泛使用的步态数据集CASIA-B、OU-MVLP、GREW和Gait3D上，以及在GaitSet、GaitPart和GaitGL三种骨干网络上进行了大量实验，证明了视角偏置的存在，并展示了我们所提方法相较于先前UDA工作的优越性。
英文摘要：
Gait Recognition is a computer vision task aiming to identify people by their walking patterns. Although existing methods often show high performance on specific datasets, they lack the ability to generalize to unseen scenarios. Unsupervised Domain Adaptation (UDA) tries to adapt a model, pre-trained in a supervised manner on a source domain, to an unlabelled target domain. There are only a few works on UDA for gait recognition proposing solutions to limited scenarios. In this paper, we reveal a fundamental phenomenon in adaptation of gait recognition models, caused by the bias in the target domain to viewing angle or walking direction. We then suggest a remedy to reduce this bias with a novel triplet selection strategy combined with curriculum learning. To this end, we present Gait Orientation-based method for Unsupervised Domain Adaptation (GOUDA). We provide extensive experiments on four widely-used gait datasets, CASIA-B, OU-MVLP, GREW, and Gait3D, and on three backbones, GaitSet, GaitPart, and GaitGL, justifying the view bias and showing the superiority of our proposed method over prior UDA works.
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。
