基于选择性融合的无监督步态识别
Unsupervised Gait Recognition with Selective Fusion
AI总结：
针对步态识别标注成本高、跨域性能下降的问题，提出无监督步态识别任务及包含选择性聚类融合与选择性样本融合的方法，有效提升了换衣和前后视角场景下的识别准确率。
摘要：
以往的步态识别方法主要在标注数据集上训练，这需要耗费大量的标注人力。然而，将预训练模型直接用于新数据集而不进行微调，会导致性能显著下降。因此，为使预训练步态识别模型能在无标注数据集上微调，我们提出了无监督步态识别（Unsupervised Gait Recognition, UGR）新任务。我们提出了一种基于聚类的基线方法，通过聚类级对比学习解决UGR问题，但进一步发现该任务面临更多挑战：其一，同一人穿着不同衣物的序列因外观变化显著，往往会被聚到不同簇中；其二，0°和180°视角拍摄的序列缺乏行走姿态特征，无法与其他视角的序列聚类到一起。为应对这些挑战，我们提出了选择性融合方法，包含选择性聚类融合（Selective Cluster Fusion, SCF）和选择性样本融合（Selective Sample Fusion, SSF）。在SCF中，我们通过多簇更新策略更新聚类级记忆库，合并同一人穿不同衣物的匹配簇；在SSF中，我们借助课程学习逐步合并前/后视角拍摄的序列。大量实验表明，我们的方法能有效提升不同外套行走条件和前/后视角条件下的rank-1准确率。
英文摘要：
Previous gait recognition methods primarily trained on labeled datasets, which require painful labeling effort. However, using a pre-trained model on a new dataset without fine-tuning can lead to significant performance degradation. So to make the pre-trained gait recognition model able to be fine-tuned on unlabeled datasets, we propose a new task: Unsupervised Gait Recognition (UGR). We introduce a new cluster-based baseline to solve UGR with cluster-level contrastive learning. But we further find more challenges this task meets. First, sequences of the same person in different clothes tend to cluster separately due to the significant appearance changes. Second, sequences taken from 0° and 180° views lack walking postures and do not cluster with sequences taken from other views. To address these challenges, we propose a Selective Fusion method, which includes Selective Cluster Fusion (SCF) and Selective Sample Fusion (SSF). With SCF, we merge matched clusters of the same person wearing different clothes by updating the cluster-level memory bank with a multi-cluster update strategy. And in SSF, we merge sequences taken from front/back views gradually with curriculum learning. Extensive experiments show the effectiveness of our method in improving the rank-1 accuracy in walking with different coats condition and front/back views conditions.
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。
