Boosting vision transformers for image retrieval

TL;DR

This paper introduces a hybrid vision transformer architecture with multi-branch global and local features, multi-layer skip connections, and enhanced locality, achieving state-of-the-art results in image retrieval.

Contribution

It presents a novel hybrid transformer design with multi-branch features and improved locality, outperforming previous methods in image retrieval tasks.

Findings

Outperforms previous global representation models across datasets.

Hybrid architecture significantly improves retrieval accuracy.

Fair comparison with prior models on all standard training sets.

Abstract

Vision transformers have achieved remarkable progress in vision tasks such as image classification and detection. However, in instance-level image retrieval, transformers have not yet shown good performance compared to convolutional networks. We propose a number of improvements that make transformers outperform the state of the art for the first time. (1) We show that a hybrid architecture is more effective than plain transformers, by a large margin. (2) We introduce two branches collecting global (classification token) and local (patch tokens) information, from which we form a global image representation. (3) In each branch, we collect multi-layer features from the transformer encoder, corresponding to skip connections across distant layers. (4) We enhance locality of interactions at the deeper layers of the encoder, which is the relative weakness of vision transformers. We train our model on all commonly used training sets and, for the first time, we make fair comparisons separately per training set. In all cases, we outperform previous models based on global representation. Public code is available at https://github.com/dealicious-inc/DToP.