Self-Paced Uncertainty Estimation for One-shot Person Re-Identification

TL;DR

This paper introduces SPUE-Net, a novel self-paced learning approach for one-shot person re-identification that effectively estimates uncertainties and iteratively refines pseudo-labels to improve model accuracy.

Contribution

The paper proposes a self-paced uncertainty estimation network with a co-operative learning strategy to reduce data and model uncertainties in one-shot Person Re-ID.

Findings

SPUE-Net outperforms state-of-the-art methods on multiple datasets.

The self-paced sampling strategy effectively expands labeled data.

Co-operative uncertainty estimation improves pseudo-label accuracy.

Abstract

The one-shot Person Re-ID scenario faces two kinds of uncertainties when constructing the prediction model from $X$ to $Y$. The first is model uncertainty, which captures the noise of the parameters in DNNs due to a lack of training data. The second is data uncertainty, which can be divided into two sub-types: one is image noise, where severe occlusion and the complex background contain irrelevant information about the identity; the other is label noise, where mislabeled affects visual appearance learning. In this paper, to tackle these issues, we propose a novel Self-Paced Uncertainty Estimation Network (SPUE-Net) for one-shot Person Re-ID. By introducing a self-paced sampling strategy, our method can estimate the pseudo-labels of unlabeled samples iteratively to expand the labeled samples gradually and remove model uncertainty without extra supervision. We divide the pseudo-label samples into two subsets to make the use of training samples more reasonable and effective. In addition, we apply a Co-operative learning method of local uncertainty estimation combined with determinacy estimation to achieve better hidden space feature mining and to improve the precision of selected pseudo-labeled samples, which reduces data uncertainty. Extensive comparative evaluation experiments on video-based and image-based datasets show that SPUE-Net has significant advantages over the state-of-the-art methods.