# Training Vision Transformers for Image Retrieval

**Authors:** Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, Hervé Jégou  
**arXiv:** [2102.05644](https://arxiv.org/abs/2102.05644)  
**Date:** 2021-02-10  
**Summary page:** https://www.summarizepaper.com/fr/paper/2102.05644v1/

## Abstract

Transformers have shown outstanding results for natural language understanding and, more recently, for image classification. We here extend this work and propose a transformer-based approach for image retrieval: we adopt vision transformers for generating image descriptors and train the resulting model with a metric learning objective, which combines a contrastive loss with a differential entropy regularizer. Our results show consistent and significant improvements of transformers over convolution-based approaches. In particular, our method outperforms the state of the art on several public benchmarks for category-level retrieval, namely Stanford Online Product, In-Shop and CUB-200. Furthermore, our experiments on ROxford and RParis also show that, in comparable settings, transformers are competitive for particular object retrieval, especially in the regime of short vector representations and low-resolution images.