PAM: Understanding Product Images in Cross Product Category Attribute Extraction

TL;DR

This paper introduces a transformer-based multimodal framework for extracting product attributes from images, text, and OCR tokens, improving accuracy across multiple categories in e-commerce.

Contribution

It presents a unified, multimodal attribute extraction model that leverages visual and textual cues, conditioned on product category, outperforming text-only methods.

Findings

15% gain in recall over text-only methods

10% improvement in F1 score

Effective across 14 product categories

Abstract

Understanding product attributes plays an important role in improving online shopping experience for customers and serves as an integral part for constructing a product knowledge graph. Most existing methods focus on attribute extraction from text description or utilize visual information from product images such as shape and color. Compared to the inputs considered in prior works, a product image in fact contains more information, represented by a rich mixture of words and visual clues with a layout carefully designed to impress customers. This work proposes a more inclusive framework that fully utilizes these different modalities for attribute extraction. Inspired by recent works in visual question answering, we use a transformer based sequence to sequence model to fuse representations of product text, Optical Character Recognition (OCR) tokens and visual objects detected in the product image. The framework is further extended with the capability to extract attribute value across multiple product categories with a single model, by training the decoder to predict both product category and attribute value and conditioning its output on product category. The model provides a unified attribute extraction solution desirable at an e-commerce platform that offers numerous product categories with a diverse body of product attributes. We evaluated the model on two product attributes, one with many possible values and one with a small set of possible values, over 14 product categories and found the model could achieve 15% gain on the Recall and 10% gain on the F1 score compared to existing methods using text-only features.