eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges

• URL: http://arxiv.org/abs/2107.05856v1
• Date: Tue, 13 Jul 2021 05:28:34 GMT
• Title: eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges
• Authors: Jiangbo Yuan, An-Ti Chiang, Wen Tang, Antonio Haro
• Abstract summary: eProduct is a benchmark dataset for training and evaluation on various visual search solutions in a real-world setting. We present eProduct as a training set and an evaluation set, where the training set contains 1.3M+ listing images with titles and hierarchical category labels, for model development. We will present eProduct's construction steps, provide analysis about its diversity and cover the performance of baseline models trained on it.
• Score: 8.204924070199866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale product recognition is one of the major applications of computer vision and machine learning in the e-commerce domain. Since the number of products is typically much larger than the number of categories of products, image-based product recognition is often cast as a visual search rather than a classification problem. It is also one of the instances of super fine-grained recognition, where there are many products with slight or subtle visual differences. It has always been a challenge to create a benchmark dataset for training and evaluation on various visual search solutions in a real-world setting. This motivated creation of eProduct, a dataset consisting of 2.5 million product images towards accelerating development in the areas of self-supervised learning, weakly-supervised learning, and multimodal learning, for fine-grained recognition. We present eProduct as a training set and an evaluation set, where the training set contains 1.3M+ listing images with titles and hierarchical category labels, for model development, and the evaluation set includes 10,000 query and 1.1 million index images for visual search evaluation. We will present eProduct's construction steps, provide analysis about its diversity and cover the performance of baseline models trained on it.

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