---
title: "Adapting Vision-Language Models for E-commerce"
description: "The authors build a curation pipeline over roughly 15M marketplace listings. 71 references."
authors: ["Matteo Nulli", "Vladimir Orshulevich", "Tala Bazazo", "Christian Herold", "Michael Kozielski", "Marcin Mazur", "Szymon Tuzel", "Cees G. M. Snoek", "Seyyed Hadi Hashemi", "Omar Javed", "Yannick Versley", "Shahram Khadivi"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)"
source: "https://aclanthology.org/2026.eacl-industry.38/"
doi: "10.18653/v1/2026.eacl-industry.38"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/nulli-2026-adapting-vision-language-models-e/"
---
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# Adapting Vision-Language Models for E-commerce Understanding at Scale

**Authors:** Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, Christian Herold, Michael Kozielski, Marcin Mazur, Szymon Tuzel, Cees G. M. Snoek, Seyyed Hadi Hashemi, Omar Javed, [Yannick Versley](https://md.research.elamur.ai/free/en/authors/yannick-versley/) and [Shahram Khadivi](https://md.research.elamur.ai/free/en/authors/shahram-khadivi/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-industry.38/) · [PDF](https://aclanthology.org/2026.eacl-industry.38.pdf) · [DOI 10.18653/v1/2026.eacl-industry.38](https://doi.org/10.18653/v1/2026.eacl-industry.38) · [OpenAlex](https://openalex.org/W7140110978)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper, from eBay and the University of Amsterdam, asks how to make general-purpose Vision-Language Models (VLMs) better at e-commerce without losing their general multimodal skills. E-commerce data centres on product attributes, often spans several images per item, and contains noisy seller-written content. The authors build a curation pipeline over roughly 15M marketplace listings. InternVL-2.5-26B writes captions for each main image, and Mistral-Small-3-24B then keeps only the seller-provided aspects that the caption, and therefore the image, actually supports. The result is about 4M e-commerce instructions. These are mixed with LLaVA-OneVision data in a three-stage training recipe that is applied across several vision encoders and text decoders. The authors also introduce four in-house benchmarks.

The adapted models gain substantially on in-domain tasks while keeping general capabilities. In a production case study, a fine-tuned small model with better labels and targeted image crops beats a much larger zero-shot model while running several times faster. The work offers a reusable recipe and an evaluation suite for multimodal shopping systems. Its scope is limited to English and to data from a single platform.

## Key points
- **Training recipe:** It has three stages modelled on LLaVA-OneVision: alignment on BLIP-LAION 558k, a reduced mid-stage mixture, and visual instruction tuning. The e-commerce instruction mix is VQA (45%), Dynamic Attribute Extraction (30%), Precise Instruction Following (12.5%) and full Listings (12.5%). Vision encoders tested include SigLIP2-SO400M-Patch14-384 and Qwen2.5 ViT.
- **Benchmark suite:**
  - Aspect Prediction: 2,600 general questions plus two Fashion subsets of 1,600 each.
  - Deep Fashion Understanding: 3,000 samples covering shirts/tops, handbags and sneakers.
  - Dynamic Attribute Extraction: about 1,000 GPT-4o-generated, human-verified examples.
  - Multi-Image Item Intelligence: 1,000 items from heavily regulated categories, scored with LLM-as-a-judge.
- **Item Intelligence fine-tuning data:** 100k multi-image items (median of 5 images) were labelled by GPT-4.1. The labels were then refined using crops from bounding boxes produced by Qwen2.5-VL-32B.
- **Speed and quality:** Zero-shot Gemma3-27B reaches an F1 of 44.8 at 25.5 s per example. Fine-tuned Gemma3-4B reaches an F1 of 50.5 at 6.7 s per example, about 3.8× faster. Targeted cropping outperforms Gemma3's "Pan & Scan" feature.
- **Limitations:** The work covers English only and data from one marketplace, relies on LLM-based supervision and evaluation, and runs into memory and latency problems with more than 10 images.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Amsterdam](https://md.research.elamur.ai/free/en/universities/university-of-amsterdam/) — University, Amsterdam, Netherlands · 144 works · 173,906 citations
- [eBay](https://md.research.elamur.ai/free/en/companies/ebay/) — Company, San Jose, United States · 2 works

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## Cite

Cite as: Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, Christian Herold, Michael Kozielski, Marcin Mazur, Szymon Tuzel, Cees G. M. Snoek, Seyyed Hadi Hashemi, Omar Javed, Yannick Versley and Shahram Khadivi. 2026. Adapting Vision-Language Models for E-commerce Understanding at Scale. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{nulli2026adapting,
  title = {{Adapting Vision-Language Models for E-commerce Understanding at Scale}},
  author = {Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, Christian Herold, Michael Kozielski, Marcin Mazur, Szymon Tuzel, Cees G. M. Snoek, Seyyed Hadi Hashemi, Omar Javed, Yannick Versley and Shahram Khadivi},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-industry.38},
  url = {https://doi.org/10.18653/v1/2026.eacl-industry.38}
}
```

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*Updated 1 November 2026*