Revolutionizing Text-to-Image Retrieval as Autoregressive Token-to-Voken Generation

TL;DR

This paper introduces AVG, a novel autoregressive method that tokenizes images into vokens and formulates text-to-image retrieval as a token-to-voken generation task, improving alignment with semantics and visual information.

Contribution

The paper proposes a new generative approach for text-to-image retrieval that uses tokenized visual representations and combines generative and discriminative training for better performance.

Findings

Achieves superior retrieval effectiveness.

Demonstrates improved efficiency over existing methods.

Effectively aligns visual tokens with high-level semantics.

Abstract

Text-to-image retrieval is a fundamental task in multimedia processing, aiming to retrieve semantically relevant cross-modal content. Traditional studies have typically approached this task as a discriminative problem, matching the text and image via the cross-attention mechanism (one-tower framework) or in a common embedding space (two-tower framework). Recently, generative cross-modal retrieval has emerged as a new research line, which assigns images with unique string identifiers and generates the target identifier as the retrieval target. Despite its great potential, existing generative approaches are limited due to the following issues: insufficient visual information in identifiers, misalignment with high-level semantics, and learning gap towards the retrieval target. To address the above issues, we propose an autoregressive voken generation method, named AVG. AVG tokenizes images into vokens, i.e., visual tokens, and innovatively formulates the text-to-image retrieval task as a token-to-voken generation problem. AVG discretizes an image into a sequence of vokens as the identifier of the image, while maintaining the alignment with both the visual information and high-level semantics of the image. Additionally, to bridge the learning gap between generative training and the retrieval target, we incorporate discriminative training to modify the learning direction during token-to-voken training. Extensive experiments demonstrate that AVG achieves superior results in both effectiveness and efficiency.