MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model
This work addresses food retrieval for health-oriented applications, representing an incremental improvement in a domain-specific task.
The paper tackles cross-modal retrieval between food images and cooking recipes by proposing MCEN, which learns modality-invariant representations using latent variables to capture alignments, achieving state-of-the-art performance on the Recipe1M dataset with reduced computational cost.
Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community. One of the most popular research topics in this domain is Food Retrieval, due to its profound influence on health-oriented applications. In this paper, we focus on the task of cross-modal retrieval between food images and cooking recipes. We present Modality-Consistent Embedding Network (MCEN) that learns modality-invariant representations by projecting images and texts to the same embedding space. To capture the latent alignments between modalities, we incorporate stochastic latent variables to explicitly exploit the interactions between textual and visual features. Importantly, our method learns the cross-modal alignments during training but computes embeddings of different modalities independently at inference time for the sake of efficiency. Extensive experimental results clearly demonstrate that the proposed MCEN outperforms all existing approaches on the benchmark Recipe1M dataset and requires less computational cost.