Dual-Imbalance Continual Learning for Real-World Food Recognition

ArXi:2603.29133v1 Announce Type: new Visual food recognition in real-world dietary logging scenarios naturally exhibits severe data imbalance, where a small number of food categories appear frequently while many others occur rarely, resulting in long-tailed class distributions. In practice, food recognition systems often operate in a continual learning setting, where new categories are