Multi-Label Learning Based on Transfer Learning and Label Correlation

Yang, Kehua; She, Chaowei; Zhang, Wei; Yao, Jiqing; Long, Shaosong

doi:10.32604/cmc.2019.05901

Cited by 6 publications

(2 citation statements)

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“…(2)  is the labeling of the motion posture feature of image 2. According to this Equation, the images in the set can be marked and sorted [18]. To set image feature statistical rules, there are Brazilian Archives of Biology and Technology.…”

Section: Feature Extraction Of Hmpmentioning

confidence: 99%

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Wang

Ren

et al. 2023

Braz. arch. biol. technol.

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Human posture estimation is the basis of many computer vision tasks, such as motion recognition, violence detection and behavior understanding. Therefore, it is of great significance to study the estimation algorithm of human motion posture (HMP). To address the problem of poor estimation effect of traditional HMP estimation algorithm, in this paper, an estimation algorithm for HMP using multi-labeling transfer learning is proposed. First, the original human motion image is labeled by using the multi-label transfer learning, the HMP features are extracted, and the original image classification is completed. Second, a regulator is constructed based on the classification results of the original image, and the regulator is used to adjust the estimation results of HMP based on a convolutional neural networks. Finally, the posture compensation function is used to compensate for the error part to realize the estimation of HMP. In the experiment, the Human3.6m data set and MPII data set were used as the basis for testing. The results show HIGHLIGHTS• This paper addresses the problem that it is difficult to mine and extract the potential features of human motion posture feature vector.• The posture compensation function is used to compensate the error part to ensure the accuracy of posture estimation results• The posture compensation function is used to correct and compensate the estimation results and improve the reliability of the estimation results.

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Section: Feature Extraction Of Hmpmentioning

confidence: 99%

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Wang

Ren

et al. 2023

Braz. arch. biol. technol.

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show abstract

“…When the source domain has a large amount of calibration data and the target domain has only a small amount of calibration data, and the source domain and the target domain have some common generalization characteristics, the transfer learning can be used to apply the data pre-training model of the source domain to the task of the target domain to improve the performance of the model in the target domain [ 29 ]. In this paper, the time series data transfer learning refers to train the CLSTM network by the time series data, and then transfer the characteristic of the trained CLSTM network to a new CLSTM network and try to fine-tune the parameters of the new network by a new time series data.…”

Section: A Clstm and Transfer Learning Based Cfdama Schemementioning

confidence: 99%

A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks

Xiang

Ren

2021

PLoS ONE

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With the development of the economy and technology, people’s requirement for communication is also increasing. Satellite communication networks have been paid more and more attention because of their broadband service capability and wide coverage. In this paper, we investigate the scheme of convolutional long short term memory (CLSTM) network and transfer learning (TL) based combined free/demand assignment multiple access (CFDAMA) scheme (CFDAMA-CLSTMTL), which is a new multiple access scheme in the satellite communication networks. Generally, there is a delay time T between sending a request from the user to the satellite and receiving a reply from the satellite. So far, the traditional multiple access schemes have not processed the data generated in this period. So, in order to transmit the data in time, we propose a new prediction method CLSTMTL, which can be used to predict the data generated in this period. We introduce the prediction method into the CFDAMA scheme so that it can reduce data accumulation by the way of sending the slots request which is the sum of slots requested by the user and the predicted slots generated in the delay time. A comparison with CFDAMA-PA and CFDAMA-PB is provided through simulation results, which gives the effect of the CFDAMA-CLSTMTL in a satellite communication network.

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Unifying Domain Adaptation into Simultaneous Discriminant Analysis: A Method for Low-Resolution Face Recognition with Single Sample Per Person

Chu

Zhao

2020

Communications in Computer and Information Science

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Multi-Label Learning Based on Transfer Learning and Label Correlation

Cited by 6 publications

References 0 publications

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks

Unifying Domain Adaptation into Simultaneous Discriminant Analysis: A Method for Low-Resolution Face Recognition with Single Sample Per Person

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