Unitail: Detecting, Reading, and Matching in Retail Scene
This addresses the need for tailored computer vision solutions in retail environments, though it is incremental as it builds on existing datasets and methods.
The authors introduced Unitail, a large-scale benchmark for product detection, reading, and matching in retail scenes, with 1.8M annotated instances and 1454 categories, and developed a custom detector and OCR-based matching solution that demonstrated effectiveness.
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.