mlsanityData to 14 Oct 2026

Unitail: Detecting, Reading, and Matching in Retail Scene

arXiv 2204.00298 ↗First posted 1 Apr 2022cs.CVcited by 2

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Abstract (arXiv)

To make full use of computer vision technology in stores, it is required to consider the actual needs that fit the characteristics of the retail scene. Pursuing this goal, we introduce the United Retail Datasets (Unitail), a large-scale benchmark of basic visual tasks on products that challenges algorithms for detecting, reading, and matching. With 1.8M quadrilateral-shaped instances annotated, the Unitail offers a detection dataset to align product appearance better. Furthermore, it provides a gallery-style OCR dataset containing 1454 product categories, 30k text regions, and 21k transcriptions to enable robust reading on products and motivate enhanced product matching. Besides benchmarking the datasets using various state-of-the-arts, we customize a new detector for product detection and provide a simple OCR-based matching solution that verifies its effectiveness.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 57 of its 80 references that are in the map. First posted on arXiv 1 Apr 2022.

Builds on

57 of 80 references.

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50 most cited of 57 All 57 (JSON)

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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Creation and Evaluation of a Food Product Image Dataset for Product Property Extractioncited by 1
  2. 2025Seeing the Signs: A Survey of Edge-Deployable OCR Models for Billboard Visibility Analysiscited by 0