Covariate shift adaptation for discriminative 3D pose estimation.

Area of Science:

• Computer Vision
• Machine Learning

Background:

• Discriminative methods are effective for computer vision tasks like 3D monocular pose estimation.
• Existing methods assume training and test data share the same distribution, which is often not true in real-world scenarios.
• Training set bias leads to degraded model performance on target test sets.

Purpose of the Study:

• To propose an unsupervised domain adaptation approach to mitigate performance degradation caused by training set bias.
• To address the problem of covariate shift in machine learning models.

Main Methods:

• Developed an instance reweighting technique for unsupervised domain adaptation.
• Assigned weights based on the ratio of training and test marginal distributions.
• Proposed weighted variants of kernel regression (KR) and twin Gaussian processes (TGP).

Main Results:

• Weighted KR and TGP models demonstrated improved performance over their unweighted counterparts.
• The proposed approach achieved state-of-the-art results on the HumanEva dataset.
• Instance reweighting effectively alleviated model bias caused by training set discrepancies.

Conclusions:

• Unsupervised domain adaptation via instance reweighting is an effective strategy for improving model generalization.
• The proposed weighted methods offer a robust solution for 3D monocular pose estimation under covariate shift.
• This approach enhances the reliability of computer vision models in diverse data distributions.