Cross-modal recipe retrieval based on unified text encoder with fine-grained contrastive learning

Abstract

Cross-modal recipe retrieval is vital for transforming visual food cues into actionable cooking guidance, making culinary creativity more accessible. Existing methods separately encode the recipe Title, Ingredient, and Instruction using different text encoders, then aggregate them to obtain recipe feature, and finally match it with encoded image feature in a joint embedding space. These methods perform well but require significant computational cost. In addition, they only consider matching the entire recipe and the image but ignore the fine-grained correspondence between recipe components and the image, resulting in insufficient cross-modal interaction. To this end, we propose Unified Text Encoder with Fine-grained Contrastive Learning (UTE-FCL) to achieve a simple but efficient model. Specifically, in each recipe, UTE-FCL first concatenates each of the Ingredient and Instruction texts composed of multiple sentences as a single text. Then, it connects these two concatenated texts with the original single-phrase Title to obtain the concatenated recipe. Finally, it encodes these three concatenated texts and the original Title by a Transformer-based Unified Text Encoder (UTE). This proposed structure greatly reduces the memory usage and improves the feature encoding efficiency. Further, we propose fine-grained contrastive learning objectives to capture the correspondence between recipe components and the image at Title, Ingredient, and Instruction levels by measuring the mutual information. Extensive experiments demonstrate the effectiveness of UTE-FCL compared to existing methods.