We propose UAPAR, an Uncertainty-Aware Pedestrian Attribute Recognition framework. To the best of our knowledge, this is the first EDL-based uncertainty-aware framework for pedestrian attribute recognition (PAR). Unlike conventional deterministic methods, which fail to assess prediction reliability on low-quality samples, UAPAR effectively identifies unreliable predictions and thus enhances system robustness in complex real-world scenarios. To achieve this, UAPAR incorporates Evidential Deep Learning (EDL) into a CLIP-based architecture. Specifically, a Region-Aware Evidence Reasoning module employs cross-attention and spatial prior masks to capture fine-grained local features, which are further processed by an evidence head to estimate attribute-wise epistemic uncertainty. To further enhance training robustness, we develop an uncertainty-guided dual-stage curriculum learning strategy to alleviate the adverse effects of severe label noise during training. Extensive experiments on the PA100K, PETA, RAPv1, and RAPv2 datasets demonstrate that UAPAR achieves competitive or superior performance. Furthermore, qualitative results confirm that the proposed framework generates uncertainty estimates that are predictive of challenging or erroneous samples.

翻译：我们提出UAPAR，一种基于不确定性的行人属性识别框架。据我们所知，这是首个基于证据深度学习（EDL）的不确定性感知框架用于行人属性识别（PAR）。与无法评估低质量样本预测可靠性的传统确定性方法不同，UAPAR有效识别不可靠预测，从而增强系统在复杂真实场景中的鲁棒性。为实现此目标，UAPAR将证据深度学习（EDL）融入基于CLIP的架构中。具体而言，区域感知证据推理模块通过交叉注意力与空间先验掩码捕捉细粒度局部特征，这些特征进一步由证据头处理以估计属性层面的认知不确定性。为提升训练鲁棒性，我们开发了一种不确定性引导的双阶段课程学习策略，以缓解训练过程中严重标签噪声的不利影响。在PA100K、PETA、RAPv1和RAPv2数据集上的大量实验表明，UAPAR取得了具有竞争力或更优的性能。此外，定性结果证实，所提框架生成的不确定性估计能够有效预测具有挑战性或错误的样本。