Multi-task Learning for Supervised and Unsupervised Classification of Grocery Images

Grocery products detection and recognition is a very complex task because of the high variability in object appearances, and the possibly very large number of the products to be recognized. Here we present the results of our investigation in the classification of grocery products. We tested several CNN architectures trained in different modalities for product classification, and we propose a multi-task learning network to be used as feature extractor. We evaluated the features extracted from the networks in both supervised and unsupervised classification scenarios. All the experiments have been conducted on publicly available datasets in the literature.