mlsanityData to 14 Oct 2026

Visual Product Graph: Bridging Visual Products And Composite Images For End-to-End Style Recommendations

arXiv 2505.21454 ↗First posted 27 May 2025cs.CVcited by 0

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Abstract (arXiv)

Retrieving semantically similar but visually distinct contents has been a critical capability in visual search systems. In this work, we aim to tackle this problem with Visual Product Graph (VPG), leveraging high-performance infrastructure for storage and state-of-the-art computer vision models for image understanding. VPG is built to be an online real-time retrieval system that enables navigation from individual products to composite scenes containing those products, along with complementary recommendations. Our system not only offers contextual insights by showcasing how products can be styled in a context, but also provides recommendations for complementary products drawn from these inspirations. We discuss the essential components for building the Visual Product Graph, along with the core computer vision model improvements across object detection, foundational visual embeddings, and other visual signals. Our system achieves a 78.8% extremely similar@1 in end-to-end human relevance evaluations, and a 6% module engagement rate. The "Ways to Style It" module, powered by the Visual Product Graph technology, is deployed in production at Pinterest.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 35 references that are in the map. First posted on arXiv 27 May 2025.

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20 of 35 references.

References · 20

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8 more
  1. 2021Embedding-based Product Retrieval in Taobao Searchcited by 51
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  3. 2019Learning a Unified Embedding for Visual Search at Pinterestcited by 25
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  8. 2022Evolution of a Web-Scale Near Duplicate Image Detection Systemcited by 1

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