Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A review on vision-based analysis for automatic dietary assessment   Background: Maintaining a healthy diet is vital to avoid health-related
issues, e.g., undernutrition, obesity and many non-communicable diseases. An
indispensable part of the health diet is dietary assessment. Traditional manual
recording methods are not only burdensome but time-consuming, and contain
substantial biases and errors. Recent advances in Artificial Intelligence (AI),
especially computer vision technologies, have made it possible to develop
automatic dietary assessment solutions, which are more convenient, less
time-consuming and even more accurate to monitor daily food intake. Scope and
approach: This review presents Vision-Based Dietary Assessment (VBDA)
architectures, including multi-stage architecture and end-to-end one. The
multi-stage dietary assessment generally consists of three stages: food image
analysis, volume estimation and nutrient derivation. The prosperity of deep
learning makes VBDA gradually move to an end-to-end implementation, which
applies food images to a single network to directly estimate the nutrition. The
recently proposed end-to-end methods are also discussed. We further analyze
existing dietary assessment datasets, indicating that one large-scale benchmark
is urgently needed, and finally highlight critical challenges and future trends
for VBDA. Key findings and conclusions: After thorough exploration, we find
that multi-task end-to-end deep learning approaches are one important trend of
VBDA. Despite considerable research progress, many challenges remain for VBDA
due to the meal complexity. We also provide the latest ideas for future
development of VBDA, e.g., fine-grained food analysis and accurate volume
estimation. This review aims to encourage researchers to propose more practical
solutions for VBDA.

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The categories of this paper are: adap-org
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The categories of this paper are: eess
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol