# Web-Scale Responsive Visual Search at Bing

arXiv: https://arxiv.org/abs/1802.04914 · HTML: https://mlsanity.com/paper/1802.04914/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2018.

## Built on by (most cited first, 13 of 13)

- [Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations](https://mlsanity.com/paper/2108.05887/) (2021)
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- [I Want This Product but Different : Multimodal Retrieval with Synthetic Query Expansion](https://mlsanity.com/paper/2102.08871/) (2021)
- [LRVS-Fashion: Extending Visual Search with Referring Instructions](https://mlsanity.com/paper/2306.02928/) (2023)
- [Visually Similar Products Retrieval for Shopsy](https://mlsanity.com/paper/2210.04560/) (2022)
- [Improve Supervised Representation Learning with Masked Image Modeling](https://mlsanity.com/paper/2312.00950/) (2023)
- [FashionFail: Addressing Failure Cases in Fashion Object Detection and Segmentation](https://mlsanity.com/paper/2404.08582/) (2024)
- [BiasEdit: A Training-Free Bias-Detect-and-Edit Framework for Learning Fair Visual Classifiers](https://mlsanity.com/paper/2605.28450/) (2026)
- [Virtual ID Discovery from E-commerce Media at Alibaba: Exploiting Richness of User Click Behavior for Visual Search Relevance](https://mlsanity.com/paper/2102.04667/) (2021)
- [Web-Scale Generic Object Detection at Microsoft Bing](https://mlsanity.com/paper/2107.01814/) (2021)
- [Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents](https://mlsanity.com/paper/2504.08739/) (2025)
- [From Pixels to Purchase: Building and Evaluating a Taxonomy-Decoupled Visual Search Engine for Home Goods E-commerce](https://mlsanity.com/paper/2601.11769/) (2026)

## Abstract (arXiv)

In this paper, we introduce a web-scale general visual search system deployed in Microsoft Bing. The system accommodates tens of billions of images in the index, with thousands of features for each image, and can respond in less than 200 ms. In order to overcome the challenges in relevance, latency, and scalability in such large scale of data, we employ a cascaded learning-to-rank framework based on various latest deep learning visual features, and deploy in a distributed heterogeneous computing platform. Quantitative and qualitative experiments show that our system is able to support various applications on Bing website and apps.