MUST: An Effective and Scalable Framework for Multimodal Search of Target Modality

TL;DR

MUST is a scalable, efficient framework for multimodal search that intelligently fuses multiple modalities using learned weights and a fused proximity graph, significantly improving accuracy and speed over baseline methods.

Contribution

The paper introduces MUST, a novel multimodal search framework that employs hybrid fusion, vector weight learning, and a fused proximity graph for improved accuracy and efficiency.

Findings

Achieves over 10x faster search times compared to baselines.

Attains an average of 93% higher accuracy in multimodal retrieval.

Scales effectively to datasets with over 10 million elements.

Abstract

We investigate the problem of multimodal search of target modality, where the task involves enhancing a query in a specific target modality by integrating information from auxiliary modalities. The goal is to retrieve relevant objects whose contents in the target modality match the specified multimodal query. The paper first introduces two baseline approaches that integrate techniques from the Database, Information Retrieval, and Computer Vision communities. These baselines either merge the results of separate vector searches for each modality or perform a single-channel vector search by fusing all modalities. However, both baselines have limitations in terms of efficiency and accuracy as they fail to adequately consider the varying importance of fusing information across modalities. To overcome these limitations, the paper proposes a novel framework, called MUST. Our framework employs a hybrid fusion mechanism, combining different modalities at multiple stages. Notably, we leverage vector weight learning to determine the importance of each modality, thereby enhancing the accuracy of joint similarity measurement. Additionally, the proposed framework utilizes a fused proximity graph index, enabling efficient joint search for multimodal queries. MUST offers several other advantageous properties, including pluggable design to integrate any advanced embedding techniques, user flexibility to customize weight preferences, and modularized index construction. Extensive experiments on real-world datasets demonstrate the superiority of MUST over the baselines in terms of both search accuracy and efficiency. Our framework achieves over 10x faster search times while attaining an average of 93% higher accuracy. Furthermore, MUST exhibits scalability to datasets containing more than 10 million data elements.