  The precise recognition of food categories plays a pivotal role for
intelligent health management, attracting significant research attention in
recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172,
provide abundant food image resources that catalyze the prosperity of research
in this field. Nevertheless, these datasets are well-curated from canteen
scenarios and thus deviate from food appearances in daily life. This
discrepancy poses great challenges in effectively transferring classifiers
trained on these canteen datasets to broader daily-life scenarios encountered
by humans. Toward this end, we present two new benchmarks, namely DailyFood-172
and DailyFood-16, specifically designed to curate food images from everyday
meals. These two datasets are used to evaluate the transferability of
approaches from the well-curated food image domain to the everyday-life food
image domain. In addition, we also propose a simple yet effective baseline
method named Multi-Cluster Reference Learning (MCRL) to tackle the
aforementioned domain gap. MCRL is motivated by the observation that food
images in daily-life scenarios exhibit greater intra-class appearance variance
compared with those in well-curated benchmarks. Notably, MCRL can be seamlessly
coupled with existing approaches, yielding non-trivial performance
enhancements. We hope our new benchmarks can inspire the community to explore
the transferability of food recognition models trained on well-curated datasets
toward practical real-life applications.
