IEEE Trans Pattern Anal Mach Intell - Covariate Shift Adaptation for Discriminative 3D Pose Estimation.

Tópicos

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Resumo

Discriminative, or (structured) prediction, methods have proved effective for variety of problems in computer vision; a notable example is 3D monocular pose estimation. All methods to date, however, relied on an assumption that training (source) and test (target) data come from the same underlying joint distribution. In many real cases, including standard datasets, this assumption is flawed. In presence of training set bias, the learning results in a biased model whose performance degrades on the (target) test set. Under the assumption of covariate shift we propose an unsupervised domain adaptation approach to address this problem. The approach takes the form of training instance re-weighting, where the weights are assigned based on the ratio of training and test marginals evaluated at the samples. Learning with the resulting weighted training samples, alleviates the bias in the learned models. We show the efficacy of our approach by proposing weighted variants of Kernel Regression (KR) and Twin Gaussian Processes (TGP). We show that our weighted variants outperform their un-weighted counterparts and improve on the state-of-the-art performance in the public (HumanEva) dataset.

Resumo Limpo

discrimin structur predict method prove effect varieti problem comput vision notabl exampl d monocular pose estim method date howev reli assumpt train sourc test target data come under joint distribut mani real case includ standard dataset assumpt flaw presenc train set bias learn result bias model whose perform degrad target test set assumpt covari shift propos unsupervis domain adapt approach address problem approach take form train instanc reweight weight assign base ratio train test margin evalu sampl learn result weight train sampl allevi bias learn model show efficaci approach propos weight variant kernel regress kr twin gaussian process tgp show weight variant outperform unweight counterpart improv stateoftheart perform public humaneva dataset

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