A large-scale benchmark for food image segmentation

• Main Authors: WU, Xiongwei, FU, Xin, LIU, Ying, LIM, Ee-peng, HOI, Steven C. H., SUN, Qianru
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Datasets; Food Computing; Semantic Segmentation; Deep Learning; Databases and Information Systems; Graphics and Human Computer Interfaces; Numerical Analysis and Scientific Computing
• 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
• Institution: Singapore Management University

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...