Training Vision Transformers for Image Retrieval

• URL: http://arxiv.org/abs/2102.05644v1
• Date: Wed, 10 Feb 2021 18:56:41 GMT
• Title: Training Vision Transformers for Image Retrieval
• Authors: Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, Herv\'e J\'egou
• Abstract summary: We adopt vision transformers for generating image descriptors and train the resulting model with a metric learning objective. Our results show consistent and significant improvements of transformers over convolution-based approaches.
• Score: 32.09708181236154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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