LLMs-based augmentation for domain adaptation in long-tailed food datasets

• Main Authors: WANG, Qing, NGO, Chong-wah, LIM, Ee-peng, SUN, Qianru
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2025
• Subjects: Class imbalance; Domain adaptation; Food recognition; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/10103; https://ink.library.smu.edu.sg/context/sis_research/article/11103/viewcontent/LLM_Based_Food_2025_av.pdf
• Institution: Singapore Management University

Training a model for food recognition is challenging because the training samples, which are typically crawled from the Internet, are visually different from the pictures captured by users in the free-living environment. In addition to this domain-shift problem, the real-world food datasets tend to...