Hierarchical Multi-Task Learning for Healthy Drink Classification

Park, Homin; Bharadhwaj, Homanga; Lim, Brian Y.

doi:10.1109/ijcnn.2019.8851796

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…Thus, mobile applications for dietary assessment should include drinks. Recognising drinks will be challenging, given the fact that drinks do not have a clear shape and are often occluded by their containers, and ingredients are often blended in the drink [56]. Thus, accurately recognising the nutritional content of a drink based solely on image seems arduous and ambitious.…”

Section: Resultsmentioning

confidence: 99%

Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

et al. 2022

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The growing awareness of the influence of “what we eat” on lifestyle and health has led to an increase in the use of embedded food analysis and recognition systems. These solutions aim to effectively monitor daily food consumption, and therefore provide dietary recommendations to enable and support lifestyle changes. Mobile applications, due to their high accessibility, are ideal for real-life food recognition, volume estimation and calorific estimation. In this study, we conducted a systematic review based on articles that proposed mobile computer vision-based solutions for food recognition, volume estimation and calorific estimation. In addition, we assessed the extent to which these applications provide explanations to aid the users to understand the related classification and/or predictions. Our results show that 90.9% of applications do not distinguish between food and non-food. Similarly, only one study that proposed a mobile computer vision-based application for dietary intake attempted to provide explanations of features that contribute towards classification. Mobile computer vision-based applications are attracting a lot of interest in healthcare. They have the potential to assist in the management of chronic illnesses such as diabetes, ensuring that patients eat healthily and reducing complications associated with unhealthy food. However, to improve trust, mobile computer vision-based applications in healthcare should provide explanations of how they derive their classifications or volume and calorific estimations.

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Section: Resultsmentioning

confidence: 99%

Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

et al. 2022

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“…Therefore, beverages should be included in datasets for dietary evaluation. However, due to their not possessing a distinct shape and possibly including intermingled components, drinks are more difficult to identify [86].…”

Section: Challengesmentioning

confidence: 99%

Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation

Crystal,

Valero,

Nino

et al. 2024

Sensors

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Diabetes has emerged as a worldwide health crisis, affecting approximately 537 million adults. Maintaining blood glucose requires careful observation of diet, physical activity, and adherence to medications if necessary. Diet monitoring historically involves keeping food diaries; however, this process can be labor-intensive, and recollection of food items may introduce errors. Automated technologies such as food image recognition systems (FIRS) can make use of computer vision and mobile cameras to reduce the burden of keeping diaries and improve diet tracking. These tools provide various levels of diet analysis, and some offer further suggestions for improving the nutritional quality of meals. The current study is a systematic review of mobile computer vision-based approaches for food classification, volume estimation, and nutrient estimation. Relevant articles published over the last two decades are evaluated, and both future directions and issues related to FIRS are explored.

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“…We followed a hard-parameter sharing approach in which all hidden layers are shared between the two NLP tasks. Although the data source was the same for the tasks, jointly learning them with shared layers helped to reduce the risk of overfitting as well as the training time Park et al, 2019;Parwez et al, 2019). This way of learning, in which the network is forced to find a shared representation that can predict both tasks, can be considered another regularisation mechanism.…”

Section: Modelling For Opinion Retrievalmentioning

confidence: 99%

Social media-based opinion retrieval for product analysis using multi-task deep neural networks

Gözüaçık

Şakar

Ozcan³

2021

Expert Systems with Applications

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Hierarchical Multi-Task Learning for Healthy Drink Classification

Cited by 9 publications

References 22 publications

Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

Mobile Computer Vision-Based Applications for Food Recognition and Volume and Calorific Estimation: A Systematic Review

Empowering Diabetics: Advancements in Smartphone-Based Food Classification, Volume Measurement, and Nutritional Estimation

Social media-based opinion retrieval for product analysis using multi-task deep neural networks

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