ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval

• URL: http://arxiv.org/abs/2408.02978v1
• Date: Tue, 6 Aug 2024 06:24:10 GMT
• Title: ASR-enhanced Multimodal Representation Learning for Cross-Domain Product Retrieval
• Authors: Ruixiang Zhao, Jian Jia, Yan Li, Xuehan Bai, Quan Chen, Han Li, Peng Jiang, Xirong Li,
• Abstract summary: E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. Due to large intra-product variance and high inter-product similarity in the broad-domain scenario, a visual-only representation is inadequate. We propose ASR-enhanced Multimodal Product Representation Learning (AMPere)
• Score: 28.13183873658186
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: E-commerce is increasingly multimedia-enriched, with products exhibited in a broad-domain manner as images, short videos, or live stream promotions. A unified and vectorized cross-domain production representation is essential. Due to large intra-product variance and high inter-product similarity in the broad-domain scenario, a visual-only representation is inadequate. While Automatic Speech Recognition (ASR) text derived from the short or live-stream videos is readily accessible, how to de-noise the excessively noisy text for multimodal representation learning is mostly untouched. We propose ASR-enhanced Multimodal Product Representation Learning (AMPere). In order to extract product-specific information from the raw ASR text, AMPere uses an easy-to-implement LLM-based ASR text summarizer. The LLM-summarized text, together with visual data, is then fed into a multi-branch network to generate compact multimodal embeddings. Extensive experiments on a large-scale tri-domain dataset verify the effectiveness of AMPere in obtaining a unified multimodal product representation that clearly improves cross-domain product retrieval.

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