One, Five, and Ten-Shot-Based Meta-Learning for Computationally Efficient Head Pose Estimation
Many real-world applications rely on head pose estimation. The performance of head pose estimation has significantly improved with techniques like convolutional neural networks (CNN). However, CNN requires a large amount of data for training. This article presents a new framework for head pose estim...
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| Published in | International journal of embedded and real-time communication systems Vol. 14; no. 1; pp. 1 - 24 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Hershey
IGI Global
01.01.2023
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| Online Access | Get full text |
| ISSN | 1947-3176 1947-3184 1947-3184 |
| DOI | 10.4018/IJERTCS.316877 |
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| Abstract | Many real-world applications rely on head pose estimation. The performance of head pose estimation has significantly improved with techniques like convolutional neural networks (CNN). However, CNN requires a large amount of data for training. This article presents a new framework for head pose estimation using computationally efficient first-order model-agnostic meta-learning (FO-MAML)-based method and compares the performance with existing MAML-based approaches. Experiments using one-shot, five-shot, and ten-shot settings are done using MAML and FO-MAML. A mean average error (MAEavg) of 7.72, 6.30, and 5.32 has been achieved in predicting head pose using MAML for one-, five-, and ten-shot settings, respectively. Similarly, MAEavg of 8.33, 6.84, and 6.23 has been achieved in predicting head pose using FO-MAML for one-, five-, and ten-shot settings, respectively. The computational complexity of an outer-loop update in MAML is found to be O(n2) whereas for FO-MAML it is O(n). |
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| AbstractList | Many real-world applications rely on head pose estimation. The performance of head pose estimation has significantly improved with techniques like convolutional neural networks (CNN). However, CNN requires a large amount of data for training. This article presents a new framework for head pose estimation using computationally efficient first-order model-agnostic meta-learning (FO-MAML)-based method and compares the performance with existing MAML-based approaches. Experiments using one-shot, five-shot, and ten-shot settings are done using MAML and FO-MAML. A mean average error (MAEavg) of 7.72, 6.30, and 5.32 has been achieved in predicting head pose using MAML for one-, five-, and ten-shot settings, respectively. Similarly, MAEavg of 8.33, 6.84, and 6.23 has been achieved in predicting head pose using FO-MAML for one-, five-, and ten-shot settings, respectively. The computational complexity of an outer-loop update in MAML is found to be O(n2) whereas for FO-MAML it is O(n). |
| Author | Joshi, Manoj Heikkonen, Jukka Pant, Dibakar Raj Kanth, Rajeev |
| AuthorAffiliation | Savonia University of Applied Sciences, Finland University of Turku, Finland Institute of Engineering, Nepal |
| AuthorAffiliation_xml | – name: Institute of Engineering, Nepal – name: Savonia University of Applied Sciences, Finland – name: University of Turku, Finland |
| Author_xml | – sequence: 1 givenname: Manoj surname: Joshi fullname: Joshi, Manoj organization: Institute of Engineering, Nepal – sequence: 2 givenname: Dibakar surname: Pant middlename: Raj fullname: Pant, Dibakar Raj organization: Institute of Engineering, Nepal – sequence: 3 givenname: Jukka surname: Heikkonen fullname: Heikkonen, Jukka organization: University of Turku, Finland – sequence: 4 givenname: Rajeev surname: Kanth fullname: Kanth, Rajeev organization: Savonia University of Applied Sciences, Finland |
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| SubjectTerms | Artificial neural networks Communication Computational efficiency Datasets Deep learning Efficiency Learning Methods Neural networks Pose estimation Sensors Surveillance |
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| Title | One, Five, and Ten-Shot-Based Meta-Learning for Computationally Efficient Head Pose Estimation |
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