# Domain invariant hierarchical embedding for grocery products recognition

## TL;DR

This paper introduces a novel hierarchical embedding method for grocery product recognition that uses a GAN to mitigate domain shift and enables recognition with minimal reference images, outperforming existing methods.

## Contribution

The paper presents an end-to-end architecture combining GANs and deep CNNs to handle large, evolving product sets and domain shifts in grocery recognition tasks.

## Key findings

- Outperforms state-of-the-art methods on grocery recognition datasets.
- Generalizes well to unseen products and different datasets.
- Effective with only one reference image per product.

## Abstract

Recognizing packaged grocery products based solely on appearance is still an open issue for modern computer vision systems due to peculiar challenges. Firstly, the number of different items to be recognized is huge (i.e., in the order of thousands) and rapidly changing over time. Moreover, there exist a significant domain shift between the images that should be recognized at test time, taken in stores by cheap cameras, and those available for training, usually just one or a few studio-quality images per product. We propose an end-to-end architecture comprising a GAN to address the domain shift at training time and a deep CNN trained on the samples generated by the GAN to learn an embedding of product images that enforces a hierarchy between product categories. At test time, we perform recognition by means of K-NN search against a database consisting of just one reference image per product. Experiments addressing recognition of products present in the training datasets as well as different ones unseen at training time show that our approach compares favourably to state-of-the-art methods on the grocery recognition task and generalize fairly well to similar ones.