CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling

Minghui Fang; Shengpeng Ji; Jialong Zuo; Hai Huang; Yan Xia; Jieming Zhu; Xize Cheng; Xiaoda Yang; Wenrui Liu; Gang Wang; Zhenhua Dong; Zhou Zhao

arXiv · 2406.17507

CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling

Minghui Fang·Shengpeng Ji·Jialong Zuo·Hai Huang·Yan Xia·Jieming Zhu·Xize Cheng·Xiaoda Yang·Wenrui Liu·Gang Wang·Zhenhua Dong·Zhou Zhao

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Abstract

Cross-modal retrieval aims to search for instances, which are semantically related to the query through the interaction of different modal data. Traditional solutions utilize a single-tower or dual-tower framework to explicitly compute the score between queries and candidates, which is challenged by training cost and inference latency with large-scale data. Inspired by the remarkable performance and efficiency of generative models, we propose a generative cross-modal retrieval framework (CART) based on coarse-to-fine semantic modeling, which assigns identifiers to each candidate and treats the generating identifier as the retrieval target. Specifically, we explore an effective coarse-to-fine scheme, combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. Further, considering the lack of explicit interaction between queries and candidates, we propose a feature fusion strategy to align their semantics. Extensive experiments demonstrate the effectiveness of the strategies in the CART, achieving excellent results in both retrieval performance and efficiency.

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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao. 2024-06-25. CART: A Generative Cross-Modal Retrieval Framework with Coarse-To-Fine Semantic Modeling. https://arxiv.org/abs/2406.17507

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