Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Discriminative Density Propagation for 3D Human Motion Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Monocular Human Motion Capture with a Mixture of Regressors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regression-based Hand Pose Estimation from Multiple Cameras
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Learning Joint Top-Down and Bottom-up Processes for 3D Visual Inference
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
A Least-squares Approach to Direct Importance Estimation
The Journal of Machine Learning Research
Gaussian process latent variable models for human pose estimation
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Adapting visual category models to new domains
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Real time head pose estimation with random regression forests
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Tabula rasa: Model transfer for object category detection
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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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.