  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.
