  Food computing is playing an increasingly important role in human daily life,
and has found tremendous applications in guiding human behavior towards smart
food consumption and healthy lifestyle. An important task under the
food-computing umbrella is retrieval, which is particularly helpful for health
related applications, where we are interested in retrieving important
information about food (e.g., ingredients, nutrition, etc.). In this paper, we
investigate an open research task of cross-modal retrieval between cooking
recipes and food images, and propose a novel framework Adversarial Cross-Modal
Embedding (ACME) to resolve the cross-modal retrieval task in food domains.
Specifically, the goal is to learn a common embedding feature space between the
two modalities, in which our approach consists of several novel ideas: (i)
learning by using a new triplet loss scheme together with an effective sampling
strategy, (ii) imposing modality alignment using an adversarial learning
strategy, and (iii) imposing cross-modal translation consistency such that the
embedding of one modality is able to recover some important information of
corresponding instances in the other modality. ACME achieves the
state-of-the-art performance on the benchmark Recipe1M dataset, validating the
efficacy of the proposed technique.
