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New C2P framework enhances multimodal attribute extraction for e-commerce

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework called Correcting to Predict (C2P) to improve the extraction of product attribute values from multimodal sources like text and images. This method treats attribute extraction as a correction process, learning to refine an initial pseudo-value using multimodal evidence. C2P demonstrated superior performance on ambiguous attributes compared to existing baselines and showed significant improvements in seller adoption, attribute completeness, and user engagement during online A/B tests on AliExpress. AI

IMPACT This framework could improve e-commerce product data accuracy and user experience by better extracting attribute values from multimodal product profiles.

RANK_REASON The cluster contains a research paper detailing a new framework for multimodal attribute value extraction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New C2P framework enhances multimodal attribute extraction for e-commerce

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The cluster contains a research paper detailing a new framework for multimodal attribute value extraction. [lever_c_demoted from research: ic=1 ai=1.0]
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paper, product
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High
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xiaoyi Zeng ·

    Correcting to Predict: Pseudo-Value Correction for Multimodal Attribute Value Extraction

    Product attribute value extraction (AVE) is a fundamental task in e-commerce, aiming to identify specific values of predefined attributes from multimodal product profiles such as text and images. While multimodal large language models (MLLMs) have shown promise for AVE, they face…

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