ACE-BERT: Adversarial Cross-modal Enhanced BERT for E-commerce Retrieval
Boxuan Zhang, Chao Wei, Yan Jin, Weiru Zhang

TL;DR
ACE-BERT is a novel multimodal retrieval model that uses adversarial training and raw image sequences to improve e-commerce product search, outperforming existing methods and increasing revenue.
Contribution
This paper introduces ACE-BERT, a cross-modal retrieval framework that leverages raw image sequences and adversarial learning to enhance multimodal representation alignment in e-commerce.
Findings
Outperforms state-of-the-art retrieval methods.
Achieves a 1.46% revenue increase in deployment.
Effectively aligns multimodal data representations.
Abstract
Nowadays on E-commerce platforms, products are presented to the customers with multiple modalities. These multiple modalities are significant for a retrieval system while providing attracted products for customers. Therefore, how to take into account those multiple modalities simultaneously to boost the retrieval performance is crucial. This problem is a huge challenge to us due to the following reasons: (1) the way of extracting patch features with the pre-trained image model (e.g., CNN-based model) has much inductive bias. It is difficult to capture the efficient information from the product image in E-commerce. (2) The heterogeneity of multimodal data makes it challenging to construct the representations of query text and product including title and image in a common subspace. We propose a novel Adversarial Cross-modal Enhanced BERT (ACE-BERT) for efficient E-commerce retrieval. In…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Domain Adaptation and Few-Shot Learning
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Attention Dropout · Linear Warmup With Linear Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · WordPiece · Weight Decay · Dropout · Label Smoothing
