A large-scale benchmark for food image segmentation

Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-gr...

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Main Authors: WU, Xiongwei, FU, Xin, LIU, Ying, LIM, Ee-peng, HOI, Steven C. H., SUN, Qianru
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Language:English
Published: Institutional Knowledge at Singapore Management University 2021
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Online Access:https://ink.library.smu.edu.sg/sis_research/6269
https://ink.library.smu.edu.sg/context/sis_research/article/7272/viewcontent/FoodSeg_MM2021_Camera_Ready.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-72722022-04-21T06:10:01Z A large-scale benchmark for food image segmentation WU, Xiongwei FU, Xin LIU, Ying LIM, Ee-peng HOI, Steven C. H. SUN, Qianru Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks—the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images 2021-10-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6269 info:doi/10.1145/3474085.3475201 https://ink.library.smu.edu.sg/context/sis_research/article/7272/viewcontent/FoodSeg_MM2021_Camera_Ready.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Datasets Food Computing Semantic Segmentation Deep Learning Databases and Information Systems Graphics and Human Computer Interfaces Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Datasets
Food Computing
Semantic Segmentation
Deep Learning
Databases and Information Systems
Graphics and Human Computer Interfaces
Numerical Analysis and Scientific Computing
spellingShingle Datasets
Food Computing
Semantic Segmentation
Deep Learning
Databases and Information Systems
Graphics and Human Computer Interfaces
Numerical Analysis and Scientific Computing
WU, Xiongwei
FU, Xin
LIU, Ying
LIM, Ee-peng
HOI, Steven C. H.
SUN, Qianru
A large-scale benchmark for food image segmentation
description Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks—the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different food images
format text
author WU, Xiongwei
FU, Xin
LIU, Ying
LIM, Ee-peng
HOI, Steven C. H.
SUN, Qianru
author_facet WU, Xiongwei
FU, Xin
LIU, Ying
LIM, Ee-peng
HOI, Steven C. H.
SUN, Qianru
author_sort WU, Xiongwei
title A large-scale benchmark for food image segmentation
title_short A large-scale benchmark for food image segmentation
title_full A large-scale benchmark for food image segmentation
title_fullStr A large-scale benchmark for food image segmentation
title_full_unstemmed A large-scale benchmark for food image segmentation
title_sort large-scale benchmark for food image segmentation
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url https://ink.library.smu.edu.sg/sis_research/6269
https://ink.library.smu.edu.sg/context/sis_research/article/7272/viewcontent/FoodSeg_MM2021_Camera_Ready.pdf
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