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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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 |
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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 |
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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 |
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WU, Xiongwei FU, Xin LIU, Ying LIM, Ee-peng HOI, Steven C. H. SUN, Qianru |
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WU, Xiongwei FU, Xin LIU, Ying LIM, Ee-peng HOI, Steven C. H. SUN, Qianru |
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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 |
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A large-scale benchmark for food image segmentation |
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A large-scale benchmark for food image segmentation |
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large-scale benchmark for food image segmentation |
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Institutional Knowledge at Singapore Management University |
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2021 |
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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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