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ConstrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals

View ORCID ProfileTianxun Zhou, Calvin Chee Hoe Cheah, View ORCID ProfileEunice Wei Mun Chin, Jie Chen, View ORCID ProfileHui Jia Farm, Eyleen Lay Keow Goh, View ORCID ProfileKeng Hwee Chiam
doi: https://doi.org/10.1101/2022.11.09.515746
Tianxun Zhou
1Bioinformatics Institute, A*STAR, Singapore
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  • ORCID record for Tianxun Zhou
Calvin Chee Hoe Cheah
2Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Eunice Wei Mun Chin
2Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Jie Chen
3School of Biological Sciences, Nanyang Technological University, Singapore
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Hui Jia Farm
1Bioinformatics Institute, A*STAR, Singapore
4Department of Computer Science, University of Oxford, Oxford, United Kingdom
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Eyleen Lay Keow Goh
2Neuroscience and Mental Health Faculty, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
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Keng Hwee Chiam
1Bioinformatics Institute, A*STAR, Singapore
3School of Biological Sciences, Nanyang Technological University, Singapore
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  • ORCID record for Keng Hwee Chiam
  • For correspondence: chiamkh@bii.a-star.edu.sg
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Posted November 10, 2022.
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ConstrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals
Tianxun Zhou, Calvin Chee Hoe Cheah, Eunice Wei Mun Chin, Jie Chen, Hui Jia Farm, Eyleen Lay Keow Goh, Keng Hwee Chiam
bioRxiv 2022.11.09.515746; doi: https://doi.org/10.1101/2022.11.09.515746
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ConstrastivePose: A contrastive learning approach for self-supervised feature engineering for pose estimation and behavorial classification of interacting animals
Tianxun Zhou, Calvin Chee Hoe Cheah, Eunice Wei Mun Chin, Jie Chen, Hui Jia Farm, Eyleen Lay Keow Goh, Keng Hwee Chiam
bioRxiv 2022.11.09.515746; doi: https://doi.org/10.1101/2022.11.09.515746

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