Uncertainty-Aware Pedestrian Attribute Recognition via Evidential Deep Learning

TL;DR

This paper introduces UAPAR, an uncertainty-aware pedestrian attribute recognition framework using evidential deep learning, improving reliability and robustness in complex real-world scenarios.

Contribution

It is the first EDL-based uncertainty-aware framework for pedestrian attribute recognition, integrating a CLIP-based architecture with a novel uncertainty-guided curriculum learning strategy.

Findings

UAPAR achieves competitive or superior performance on multiple datasets.

The framework effectively identifies unreliable predictions and challenging samples.

Uncertainty estimates correlate with prediction errors and sample difficulty.

Abstract

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.