Beyond Text: Aligning Vision and Language for Multimodal E-Commerce Retrieval

2026-03-05

AI Summary

This work addresses the limitations of existing e-commerce retrieval systems, which predominantly rely on textual information and struggle to effectively incorporate visual semantics from product images, thereby constraining cross-modal representation capabilities. To overcome this, the authors propose a two-stage alignment strategy tailored for e-commerce scenarios and introduce a novel vision-language fusion network that jointly optimizes multimodal representations of queries and products within a dual-tower architecture. By integrating domain-adaptive fine-tuning with a cross-modal alignment mechanism, the approach significantly enhances semantic complementarity between text and image modalities. Extensive experiments on a large-scale real-world e-commerce dataset demonstrate that the proposed method substantially outperforms text-only baselines and alternative multimodal fusion approaches, confirming its effectiveness and practical applicability.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Modern e-commerce search is inherently multimodal: customers make purchase decisions by jointly considering product text and visual informations. However, most industrial retrieval and ranking systems primarily rely on textual information, underutilizing the rich visual signals available in product images. In this work, we study unified text-image fusion for two-tower retrieval models in the e-commerce domain. We demonstrate that domain-specific fine-tuning and two stage alignment between query with product text and image modalities are both crucial for effective multimodal retrieval. Building on these insights, we propose a noval modality fusion network to fuse image and text information and capture cross-modal complementary information. Experiments on large-scale e-commerce datasets validate the effectiveness of the proposed approach.

Problem

• multimodal retrieval
• e-commerce
• vision-language alignment
• image-text fusion
• product search

Innovation

• multimodal retrieval
• two-tower model
• cross-modal alignment
• e-commerce search
• modality fusion