Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Jiangpeng He"'
Publikováno v:
Nutrients, Vol 15, Iss 14, p 3183 (2023)
New imaging technologies to identify food can reduce the reporting burden of participants but heavily rely on the quality of the food image databases to which they are linked to accurately identify food images. The objective of this study was to deve
Externí odkaz:
https://doaj.org/article/903f8a72e5f14e27a0df68ea7d05860a
Publikováno v:
Nutrients, Vol 15, Iss 12, p 2751 (2023)
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, foods in real-world scenarios are typically long-tail distributed, where a small number of food types are consu
Externí odkaz:
https://doaj.org/article/c9e5579b52114a888093ff08c97c9732
Publikováno v:
Nutrients; Volume 15; Issue 12; Pages: 2751
Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, foods in real-world scenarios are typically long-tail distributed, where a small number of food types are consu
Food image analysis is the groundwork for image-based dietary assessment, which is the process of monitoring what kinds of food and how much energy is consumed using captured food or eating scene images. Existing deep learning-based methods learn the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eb9edfb8ec7910f84eb9c82ee4eb7da7
Autor:
Zeman Shao, Jiangpeng He, Ya-Yuan Yu, Luotao Lin, Alexandra E. Cowan, Heather A. Eicher-Miller, Fengqing Zhu
Food classification is critical to the analysis of nutrients comprising foods reported in dietary assessment. Advances in mobile and wearable sensors, combined with new image based methods, particularly deep learning based approaches, have shown grea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d124246a2c908aa82089765f61142f71
http://arxiv.org/abs/2206.02086
http://arxiv.org/abs/2206.02086
Autor:
Jiangpeng He, Fengqing Zhu
Targeted for real world scenarios, online continual learning aims to learn new tasks from sequentially available data under the condition that each data is observed only once by the learner. Though recent works have made remarkable achievements by st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b2aa9d9b9af289f3bff5b5303ec04b46
Autor:
Jiangpeng He, Fengqing Zhu
Publikováno v:
Continual Semi-Supervised Learning ISBN: 9783031175862
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e54212ad570d753a390cf3b2618e566
https://doi.org/10.1007/978-3-031-17587-9_2
https://doi.org/10.1007/978-3-031-17587-9_2
Autor:
Jiangpeng He, Fengqing Zhu, Zeman Shao, Deborah A. Kerr, Runyu Mao, Carol J. Boushey, Janine Wright, Yue Han
Publikováno v:
Proceedings of the 3rd Workshop on AIxFood.
Accurate assessment of dietary intake requires improved tools to overcome limitations of current methods including user burden and measurement error. Emerging technologies such as image-based approaches using advanced machine learning techniques coup
Autor:
Fengqing Zhu, Jiangpeng He
Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially available new food images. Online continual learning aims to learn new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::942878941706c25f9bdf2136a2d172b9
http://arxiv.org/abs/2108.06781
http://arxiv.org/abs/2108.06781