Unifying Two-Stream Encoders with Transformers for Cross-Modal Retrieval

TL;DR

This paper introduces Hierarchical Alignment Transformers (HAT), a unified two-stream Transformer framework for cross-modal retrieval that improves semantic alignment between images and texts, achieving state-of-the-art results on benchmark datasets.

Contribution

The paper proposes a novel unified Transformer-based architecture for both image and text encoders, enabling better semantic alignment and multi-level correspondence exploration in cross-modal retrieval.

Findings

HAT outperforms SOTA methods on MSCOCO and Flickr30K datasets.

HAT achieves 7.6% and 16.7% improvements in Recall@1 on MSCOCO.

HAT achieves 4.4% and 11.6% improvements in Recall@1 on Flickr30K.

Abstract

Most existing cross-modal retrieval methods employ two-stream encoders with different architectures for images and texts, \textit{e.g.}, CNN for images and RNN/Transformer for texts. Such discrepancy in architectures may induce different semantic distribution spaces and limit the interactions between images and texts, and further result in inferior alignment between images and texts. To fill this research gap, inspired by recent advances of Transformers in vision tasks, we propose to unify the encoder architectures with Transformers for both modalities. Specifically, we design a cross-modal retrieval framework purely based on two-stream Transformers, dubbed \textbf{Hierarchical Alignment Transformers (HAT)}, which consists of an image Transformer, a text Transformer, and a hierarchical alignment module. With such identical architectures, the encoders could produce representations with more similar characteristics for images and texts, and make the interactions and alignments between them much easier. Besides, to leverage the rich semantics, we devise a hierarchical alignment scheme to explore multi-level correspondences of different layers between images and texts. To evaluate the effectiveness of the proposed HAT, we conduct extensive experiments on two benchmark datasets, MSCOCO and Flickr30K. Experimental results demonstrate that HAT outperforms SOTA baselines by a large margin. Specifically, on two key tasks, \textit{i.e.}, image-to-text and text-to-image retrieval, HAT achieves 7.6\% and 16.7\% relative score improvement of Recall@1 on MSCOCO, and 4.4\% and 11.6\% on Flickr30k respectively. The code is available at \url{https://github.com/LuminosityX/HAT}.