Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images (1905.01273v1)

Abstract: 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.

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

The paper "Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images" offers a novel approach to solving the problem of cross-modal retrieval in the food domain. By leveraging adversarial networks, the authors propose a method designed to learn and align embeddings across disparate data modalities—specifically, cooking recipes and food images. This effort underscores the growing importance of food computing in impacting health-related areas, including the potential for smarter dietary choices and lifestyle improvements.

The key innovation presented in the paper is the Adversarial Cross-Modal Embedding (ACME) framework. ACME reconciles the domain discrepancies between modalities by mapping both recipes and images into a joint embedding space. The framework incorporates three critical components:

1. Improved Triplet Loss with Hard Sample Mining: A new triplet loss scheme is introduced, bolstered by a robust sample mining strategy, enhancing model performance and convergence. This method particularly addresses the challenge of high variance in image appearances linked to the same recipe.
2. Modality Alignment via Adversarial Loss: An adversarial loss mechanism is employed to ensure that feature distributions from the two distinct modalities become indistinguishable. This alignment ameliorates the media gap commonly found between feature representations from different domains.
3. Cross-Modal Translation Consistency: This innovation ensures that the learned representations preserve cross-modal semantics. By generating food images from recipe embeddings and predicting ingredients from image embeddings, the framework enforces a level of translation consistency that maintains semantic alignment across modalities.

The authors validate their methodology using the Recipe1M dataset, achieving state-of-the-art results for cross-modal retrieval tasks between images and recipes. Their results demonstrate a median retrieval rank of 1.0 in a 1k test setup, with substantial performance improvements across R@K metrics compared to baselines like JE and AdaMine. Notably, ACME substantially reduces the median rank in larger 10k test setups, underscoring its efficacy even in challenging retrieval scenarios.

This work carries significant implications for the future of food computing, suggesting potential advancements in how we automate dietary assessments and recommendations using visual data. Furthermore, by successfully demonstrating cross-modal embedding using adversarial strategies, the paper opens the door to broader applications in domains where multi-modal data must be synthesized, such as medical imaging or social media analytics.

In conclusion, the ACME framework provides a compelling approach for addressing cross-modal retrieval challenges in food computing by synthesizing techniques from adversarial learning and embedding alignment. Given the results and methods described, future research might focus on extending these principles to other domains with complex multi-modal data needs, continuously refining the model architectures to enhance generalization and retrieval performance further.