Simple but Effective Raw-Data Level Multimodal Fusion for Composed Image Retrieval

TL;DR

This paper introduces a raw-data level multimodal fusion method for composed image retrieval, leveraging a VLP model with query unification to improve retrieval accuracy by maintaining the integrity of the original embedding space.

Contribution

The work proposes a novel raw-data level fusion approach using query unification, shifting from feature-level fusion, to better utilize VLP models in composed image retrieval.

Findings

Outperforms existing methods on four real-world datasets.

Effective in maintaining embedding space integrity.

Improves retrieval accuracy with simple linear combination.

Abstract

Composed image retrieval (CIR) aims to retrieve the target image based on a multimodal query, i.e., a reference image paired with corresponding modification text. Recent CIR studies leverage vision-language pre-trained (VLP) methods as the feature extraction backbone, and perform nonlinear feature-level multimodal query fusion to retrieve the target image. Despite the promising performance, we argue that their nonlinear feature-level multimodal fusion may lead to the fused feature deviating from the original embedding space, potentially hurting the retrieval performance. To address this issue, in this work, we propose shifting the multimodal fusion from the feature level to the raw-data level to fully exploit the VLP model's multimodal encoding and cross-modal alignment abilities. In particular, we introduce a Dual Query Unification-based Composed Image Retrieval framework (DQU-CIR), whose backbone simply involves a VLP model's image encoder and a text encoder. Specifically, DQU-CIR first employs two training-free query unification components: text-oriented query unification and vision-oriented query unification, to derive a unified textual and visual query based on the raw data of the multimodal query, respectively. The unified textual query is derived by concatenating the modification text with the extracted reference image's textual description, while the unified visual query is created by writing the key modification words onto the reference image. Ultimately, to address diverse search intentions, DQU-CIR linearly combines the features of the two unified queries encoded by the VLP model to retrieve the target image. Extensive experiments on four real-world datasets validate the effectiveness of our proposed method.