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Unseen head pose prediction using densemultivariate label distribution Project supported by the National Key Scientific Instrument and Equipment Development Project of China (No. 2013YQ49087903) and the National Natural Science Foundation of China (No. 61202160)

Gao-li SANG,Hu CHEN,Ge HUANG,Qi-jun ZHAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 6, Pages 516-526 doi: 10.1631/FITEE.1500235

Abstract: 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.

Keywords： Head pose estimation Dense multivariate label distribution Sampling intervals Inconsistent labels

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labelsPseudo labels are automatically generated for and used as if they were true labels.constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels

Keywords：命名实体识别；无标注数据；深度学习；半监督学习方法

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A novel algorithm to counter cross-eye jamming based on a multi-target model Research Articles

Zhi-yong SONG, Xing-lin SHEN, Qiang FU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 7, Pages 988-1001 doi: 10.1631/FITEE.1800394

Abstract: Particle identity labels that represent the properties of target and jamming are introduced into thedistinction between true targets and false jamming is realized through correlation and transmission between labels

Keywords： Particle identity labels Probability hypothesis density Cross-eye jamming Anti-jamming Random finite