# Domain invariant hierarchical embedding for grocery products recognition

arXiv: https://arxiv.org/abs/1902.00760 · HTML: https://mlsanity.com/paper/1902.00760/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2019.

## Built on by (most cited first, 5 of 5)

- [RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification](https://mlsanity.com/paper/2312.10282/) (2023)
- [Using Contrastive Learning and Pseudolabels to learn representations for Retail Product Image Classification](https://mlsanity.com/paper/2110.03639/) (2021)
- [Re-thinking Human Activity Recognition with Hierarchy-aware Label Relationship Modeling](https://mlsanity.com/paper/2403.05557/) (2024)
- [What Matters for Grocery Product Retrieval with Open Source Vision Language Models](https://mlsanity.com/paper/2605.18029/) (2026)
- [When Does Geometric View Synthesis Help Wine Label Retrieval? A Public One-Shot Benchmark Across Self-Supervised and Vision-Language Backbones](https://mlsanity.com/paper/2609.33359/) (2026)

## Abstract (arXiv)

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.