# Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products

Source: [Acquire, Augment, Segment & Enjoy: Weakly Supervised Instance Segmentation of Supermarket Products](<https://www.sota2.com/research/paper/68fa64851e209131cea051ad>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:06:03.000Z

Authors: Patrick Follmann, Bertram Drost, Tobias B\\"ottger

Published: 2018-07-05T13:38:55.000Z

## Abstract

Grocery stores have thousands of products that are usually identified using barcodes with a human in the loop. For automated checkout systems, it is necessary to count and classify the groceries efficiently and robustly. One possibility is to use a deep learning algorithm for instance-aware semantic segmentation. Such methods achieve high accuracies but require a large amount of annotated training data.   We propose a system to generate the training annotations in a weakly supervised manner, drastically reducing the labeling effort. We assume that for each training image, only the object class is known. The system automatically segments the corresponding object from the background. The obtained training data is augmented to simulate variations similar to those seen in real-world setups.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.02001>)

[PDF](<https://arxiv.org/pdf/1807.02001.pdf>)