Unsupervised Few-Shot Food Recognition With Intra-Class Variation and Inter-Class Similarity Modeling

Na Zheng, Xuemeng Song, Wai Teng Tang, See-Kiong Ng, Liqiang Nie, Roger Zimmermann

Abstract: Few-shot food recognition aims to first train a meta-model based on an extensive labeled dataset, and then adapt it to recognize novel food classes with limited labeled data. Although existing studies have achieved compelling success, they still heavily relied on a large number of labeled food data for training the initial meta-model. To save the annotation cost, we propose the unsupervised food recognition task, which aims to train a meta-model using only unlabeled food data. Due to the two challenges presented in food images: 1) high intra-class variations and 2) high inter-class similarity, directly applying existing unsupervised few-shot learning methods could result in sub-optimal results. Towards this end, we propose a novel framework, i.e., Unsupervsied Few-shot Food Recognition with Intra-class Variation and Inter-class Similarity (UFFR-IVIS). It consists of two key components: 1) dual diversity-injected support/query representation learning that introduces instance-level and representation-level diversities for the representation learning of support/query instance to model the characteristics of high intra-class variation; and 2) dual regularization-enhanced meta learning that designs two regularizations: auxiliary task-based intra-class regularization and similarity-guided inter-class regularization to regularize the intra-class variation and inter-class similarity modeling, respectively. Extensive experiments on two food datasets demonstrate the superiority of our UFFR-IVIS.

External IDs:dblp:journals/tcsv/ZhengSTNNZ25