Abstract
Gait recognition is a computer vision task that identifies individuals based on their walking patterns. Gait recognition performance is commonly evaluated by ranking a gallery of candidates and measuring the accuracy at the top Rank-$K$. Existing models are typically single-staged, i.e. searching for the probe's nearest neighbors in a gallery using a single global feature representation. Although these models typically excel at retrieving the correct identity within the top-$K$ predictions, they struggle when hard negatives appear in the top short-list, leading to relatively low performance at the highest ranks (e.g., Rank-1). In this paper, we introduce CarGait, a Cross-Attention Re-ranking method for gait recognition, that involves re-ordering the top-$K$ list leveraging the fine-grained correlations between pairs of gait sequences through cross-attention between gait strips. This re-ranking scheme can be adapted to existing single-stage models to enhance their final results. We demonstrate the capabilities of CarGait by extensive experiments on three common gait datasets, Gait3D, GREW, and OU-MVLP, and seven different gait models, showing consistent improvements in Rank-1,5 accuracy, superior results over existing re-ranking methods, and strong baselines.
Abstract (translated)
步态识别是一种基于个人行走模式来识别个体的计算机视觉任务。步态识别性能通常通过在候选图库中对目标进行排名并测量前$K$位的准确率来进行评估。现有的模型通常是单阶段的，即利用单一全局特征表示在一个图库中为探针寻找最近邻。尽管这些模型通常擅长在前$K$个预测中检索正确的身份，但在顶级短名单中出现硬负样本时会遇到困难，导致最高排名（例如，Rank-1）下的表现相对较低。 在这篇论文中，我们介绍了CarGait，一种用于步态识别的跨注意力重排序方法。该方法通过使用步态条之间的跨注意力来利用步态序列对之间细微的相关性，并重新排列前$K$位列表。这种重排序方案可以适应现有的单阶段模型以增强其最终结果。 我们在三个常见的步态数据集（Gait3D、GREW和OU-MVLP）上进行了广泛的实验，以及七种不同的步态模型，展示了在Rank-1和Rank-5准确率上的持续改进，超过现有重排序方法的优越结果，并且优于强大的基准。
URL
https://arxiv.org/abs/2503.03501
