Food Recognition with Visual Transformers

Interactive technologies that shape the traditional human-food experiences are being explored under the emerging field of Human-Food Interaction (HFI). A key challenge in developing HFI technologies is the digital sensing of food, beverages, and their ingredients, commonly known as digital food sensing. Digital food sensing involves recognizing different food and beverages and their internal attributes, such as volume and ingredients (e.g., salt and sugar content). Contemporary research on interactive food applications, such as dietary assessment, primarily employs Computer Vision (CV) techniques to identify food; however, they are ineffective when 1) identifying food’s internal attributes, 2) discriminating visually similar food and beverages, and 3) seamlessly integrating with people’s natural interactions while consuming food. Thus, this paper reviews potential food and beverage sensing technologies that can facilitate novel Human-Food Interfaces, primarily focusing on non-disruptive sensing techniques to analyze food and beverages during consumption. First, we review ten different digital food sensing techniques and their applications in four categories. Then, we discuss three main aspects to consider when adopting these food-sensing techniques for human-food interface designs. Finally, we suggest the future research requirements in digital food sensing methodologies, followed by potential applications of digital food sensing in future developments of Human-Food Interfaces.

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The Explainability of Transformers: Current Status and Directions

Fantozzi,

Naldi

2024

Computers

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An increasing demand for model explainability has accompanied the widespread adoption of transformers in various fields of applications. In this paper, we conduct a survey of the existing literature on the explainability of transformers. We provide a taxonomy of methods based on the combination of transformer components that are leveraged to arrive at the explanation. For each method, we describe its mechanism and survey its applications. We find out that attention-based methods, both alone and in conjunction with activation-based and gradient-based methods, are the most employed ones. A growing attention is also devoted to the deployment of visualization techniques to help the explanation process.

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Food Recognition with Visual Transformers

Cited by 3 publications

References 17 publications

Efficient Deep Learning Methods for Food Localization in Canteen Trays

Efficient Deep Learning Methods for Food Localization in Canteen Trays

Digital Food Sensing and Ingredient Analysis Techniques to Facilitate Human-Food Interface Designs

The Explainability of Transformers: Current Status and Directions

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