Multimodal Food Learning

Food-centered study has received more attention in the multimedia community for its profound impact on our survival, nutrition and health, pleasure, and enjoyment. Our experience of food is typically multi-sensory: We see food objects, smell its odors, taste its flavors, feel its texture, and hear sounds when chewing. Therefore, multimodal food learning is vital in food-centered study, which aims to relate information from multiple food modalities to support various multimedia tasks, ranging from recognition, retrieval, generation, recommendation, and interaction, enabling applications in different fields like healthcare and agriculture. However, there is no surveys on this topic to our knowledge. To fill this gap, this article formalizes multimodal food learning and comprehensively surveys its typical tasks, technical achievements, existing datasets, and applications to provide the blueprint with researchers and practitioners. Based on the current state of the art, we identify both open research issues and promising research directions, such as multimodal food learning benchmark construction, multimodal food foundation model construction, and multimodality diet estimation. We also point out that closer cooperation from researchers between multimedia and food science can handle some existing challenges and meanwhile open up more new opportunities to advance the fast development of multimodal food learning. This is the first comprehensive survey in this topic and we anticipate about 170 reviewed research articles can benefit academia and industry in this community and beyond.