Beschreibung
Entity Matching (EM) defines the task of learning to group objects by transferring semantic concepts from example groups (=entities) to unseen data. Despite the general availability of image data in the context of many EM-problems, most currently available EM-algorithms solely rely on (textual) meta data.
In this paper, we introduce the first publicly available large-scale dataset for “visual entity matching”, based on a production level use case in the retail domain. Using scanned advertisement leaflets, collected over several years from different European retailers, we provide a total of ∼786k manually annotated, high resolution product images containing ∼18k different individual retail products which are grouped into ∼3k entities. The annotation of these product entities is based on a price comparison task, where each entity forms an equivalence class of comparable products. Following on a first baseline evaluation, we show that the proposed “visual entity matching” constitutes a novel learning problem which can not sufficiently be solved using standard image based classification and retrieval algorithms. Instead, novel approaches which allow to transfer example based visual equivalent classes to new data are needed to address the proposed problem. The aim of this paper is to provide a benchmark for such algorithms.
Information about the dataset, evaluation code and download instructions are provided under the website: https://www.retail-786k.org/.
| Original Venue (Nectar only) | Data-centric Machine Learning Research (DMLR) Workshop at ICLR 2024 |
|---|---|
| URL of original paper (Nectar only) | https://openreview.net/pdf/92120b80a65bab39f98fec4245f686b6c2d5a1b7.pdf |
