mlsanityData to 7 Oct 2026

Unified Vision-Language Representation Modeling for E-Commerce Same-Style Products Retrieval

arXiv 2302.05093 ↗First posted 10 Feb 2023cs.IR cs.AI cs.MMcited by 5

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Abstract (arXiv)

Same-style products retrieval plays an important role in e-commerce platforms, aiming to identify the same products which may have different text descriptions or images. It can be used for similar products retrieval from different suppliers or duplicate products detection of one supplier. Common methods use the image as the detected object, but they only consider the visual features and overlook the attribute information contained in the textual descriptions, and perform weakly for products in image less important industries like machinery, hardware tools and electronic component, even if an additional text matching module is added. In this paper, we propose a unified vision-language modeling method for e-commerce same-style products retrieval, which is designed to represent one product with its textual descriptions and visual contents. It contains one sampling skill to collect positive pairs from user click log with category and relevance constrained, and a novel contrastive loss unit to model the image, text, and image+text representations into one joint embedding space. It is capable of cross-modal product-to-product retrieval, as well as style transfer and user-interactive search. Offline evaluations on annotated data demonstrate its superior retrieval performance, and online testings show it can attract more clicks and conversions. Moreover, this model has already been deployed online for similar products retrieval in alibaba.com, the largest B2B e-commerce platform in the world.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 19 references that are in the map. First posted on arXiv 10 Feb 2023.

Builds on

17 of 19 references.

References · 17

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5 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025ImageScope: Unifying Language-Guided Image Retrieval via Large Multimodal Model Collective Reasoningcited by 12
  2. 2025UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusioncited by 10
  3. 2025OneVision: An End-to-End Generative Framework for Multi-view E-commerce Vision Searchcited by 2
  4. 2025FLUID: Flow-Latent Unified Integration via Token Distillation for Expert Specialization in Multimodal Learningcited by 1
  5. 2026TIGER-FG: Text-Guided Implicit Fine-Grained Grounding for E-commerce Retrievalcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance
  2. 2023MAKE: Vision-Language Pre-training based Product Retrieval in Taobao Search
  3. 2022V2L: Leveraging Vision and Vision-language Models into Large-scale Product Retrieval
  4. 2025Improving Visual Recommendation on E-commerce Platforms Using Vision-Language Models
  5. 2025Turning Adversaries into Allies: Reversing Typographic Attacks for Multimodal E-Commerce Product Retrieval
  6. 2026FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data
  7. 2025PRISM: Product Retrieval In Shopping Carts using Hybrid Matching
  8. 2023Delving into E-Commerce Product Retrieval with Vision-Language Pre-training