1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert ...1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert knowledge.In many practical application scenarios,labeled and unlabeled samples exist simultaneously,with more unlabeled than labeled samples in streaming data[5,6].Unfortunately,existing class-incremental learning methods face limitations in effectively utilizing unlabeled data,thereby impeding their performance in incremental learning scenarios.展开更多
Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previous...Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.展开更多
文摘1 Introduction Current continual learning methods[1–4]can utilize labeled data to alleviate catastrophic forgetting effectively.However,obtaining labeled samples can be difficult and tedious as it may require expert knowledge.In many practical application scenarios,labeled and unlabeled samples exist simultaneously,with more unlabeled than labeled samples in streaming data[5,6].Unfortunately,existing class-incremental learning methods face limitations in effectively utilizing unlabeled data,thereby impeding their performance in incremental learning scenarios.
基金supported by the National Key Scientific Instrument and Equipment Development Project of China(No.2013YQ49087903)the National Natural Science Foundation of China(No.61202160)
文摘Accurate head poses are useful for many face-related tasks such as face recognition, gaze estimation,and emotion analysis. Most existing methods estimate head poses that are included in the training data(i.e.,previously seen head poses). To predict head poses that are not seen in the training data, some regression-based methods have been proposed. However, they focus on estimating continuous head pose angles, and thus do not systematically evaluate the performance on predicting unseen head poses. In this paper, we use a dense multivariate label distribution(MLD) to represent the pose angle of a face image. By incorporating both seen and unseen pose angles into MLD, the head pose predictor can estimate unseen head poses with an accuracy comparable to that of estimating seen head poses. On the Pointing'04 database, the mean absolute errors of results for yaw and pitch are 4.01?and 2.13?, respectively. In addition, experiments on the CAS-PEAL and CMU Multi-PIE databases show that the proposed dense MLD-based head pose estimation method can obtain the state-of-the-art performance when compared to some existing methods.
