Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval

• URL: http://arxiv.org/abs/2210.13440v1
• Date: Mon, 24 Oct 2022 17:53:20 GMT
• Title: Reliability-Aware Prediction via Uncertainty Learning for Person Image Retrieval
• Authors: Zhaopeng Dou, Zhongdao Wang, Weihua Chen, Yali Li, and Shengjin Wang
• Abstract summary: UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction.
• Score: 51.83967175585896
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current person image retrieval methods have achieved great improvements in accuracy metrics. However, they rarely describe the reliability of the prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL) method to remedy this issue. UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simultaneously. Data uncertainty captures the ``noise" inherent in the sample, while model uncertainty depicts the model's confidence in the sample's prediction. Specifically, in UAL, (1) we propose a sampling-free data uncertainty learning method to adaptively assign weights to different samples during training, down-weighting the low-quality ambiguous samples. (2) we leverage the Bayesian framework to model the model uncertainty by assuming the parameters of the network follow a Bernoulli distribution. (3) the data uncertainty and the model uncertainty are jointly learned in a unified network, and they serve as two fundamental criteria for the reliability assessment: if a probe is high-quality (low data uncertainty) and the model is confident in the prediction of the probe (low model uncertainty), the final ranking will be assessed as reliable. Experiments under the risk-controlled settings and the multi-query settings show the proposed reliability assessment is effective. Our method also shows superior performance on three challenging benchmarks under the vanilla single query settings.

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