arXiv:2408.02978v1 Announce Type: new
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.
Expanding the Concept of Multimedia-Enriched E-commerce
E-commerce has evolved greatly in recent years, with products being showcased in various multimedia formats such as images, short videos, and even live stream promotions. This broad-domain approach allows for a more engaging and immersive shopping experience for consumers. However, to effectively represent these products across different domains, a unified and vectorized cross-domain production representation is crucial.
The challenge lies in the fact that there is often significant variation within products themselves, while still maintaining high similarity to other products in the same domain. Simply relying on visual representation alone is insufficient in this broad-domain scenario. This is where Automatic Speech Recognition (ASR) can play a crucial role.
ASR-Enhanced Multimodal Product Representation Learning (AMPere)
To address the limitations of visual-only representation, the proposed solution is ASR-enhanced Multimodal Product Representation Learning, or AMPere. The goal of AMPere is to utilize the readily accessible ASR text derived from short videos or live streams and leverage it to enhance the multimodal representation learning process. However, the challenge lies in de-noising the often noisy ASR text.
AMPere tackles this challenge by employing an easy-to-implement LLM-based ASR text summarizer, which effectively extracts product-specific information from the raw ASR text. This summarized text is then combined with the visual data and fed into a multi-branch network, resulting in the generation of compact multimodal embeddings.
The Importance of Cross-Domain Product Retrieval
Extensive experiments on a large-scale tri-domain dataset validate the effectiveness of AMPere in obtaining a unified multimodal product representation, which in turn improves cross-domain product retrieval. This is critical in the context of e-commerce, as it allows for more accurate and efficient product recommendations and search results.
The concepts discussed in this article highlight the multi-disciplinary nature of multimedia information systems. By combining elements from fields such as computer vision, natural language processing, and machine learning, AMPere offers a comprehensive approach to addressing the complexities of representing and retrieving products in a multimedia-enriched e-commerce environment.
Link to Other Related Fields
AMPere’s integration of ASR technology is directly related to the field of Artificial Reality (AR) and Augmented Reality (AR), as it enhances the immersive experience by intelligently incorporating text-based information into the representation of virtual products. Additionally, the focus on multimodal embeddings aligns with the broader field of Virtual Realities (VR), where the goal is to create realistic and interactive virtual environments.
In conclusion, the development of AMPere showcases the importance of a holistic, multi-disciplinary approach in advancing the capabilities of multimedia information systems within the realm of e-commerce. By effectively leveraging ASR technology and incorporating it into the learning process, AMPere takes a significant step towards achieving a unified and comprehensive representation of products in a multimedia-enriched e-commerce landscape.