Disambiguity and Alignment: An Effective Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval

Author:

Zou Zhuoyang1ORCID,Zhu Xinghui1ORCID,Zhu Qinying1,Zhang Hongyan1,Zhu Lei1ORCID

Affiliation:

1. College of Information and Intelligence, Hunan Agricultural University, Changsha 410128, China

Abstract

As a prominent topic in food computing, cross-modal recipe retrieval has garnered substantial attention. However, the semantic alignment across food images and recipes cannot be further enhanced due to the lack of intra-modal alignment in existing solutions. Additionally, a critical issue named food image ambiguity is overlooked, which disrupts the convergence of models. To these ends, we propose a novel Multi-Modal Alignment Method for Cross-Modal Recipe Retrieval (MMACMR). To consider inter-modal and intra-modal alignment together, this method measures the ambiguous food image similarity under the guidance of their corresponding recipes. Additionally, we enhance recipe semantic representation learning by involving a cross-attention module between ingredients and instructions, which is effective in supporting food image similarity measurement. We conduct experiments on the challenging public dataset Recipe1M; as a result, our method outperforms several state-of-the-art methods in commonly used evaluation criteria.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province

Scientific Research Project of Hunan Provincial Department of Education

Publisher

MDPI AG

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