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Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images

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arxiv 1810.06553 v2 pith:WZOBOHHO submitted 2018-10-14 cs.CV

Javier Marin , Aritro Biswas , Ferda Ofli , Nicholas Hynes , Amaia Salvador , Yusuf Aytar , Ingmar Weber , Antonio Torralba This is my paper · ORCID
classification cs.CV
keywords datarecipe1mcookingfoodimagesrecipesavailabledataset
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In this paper, we introduce Recipe1M+, a new large-scale, structured corpus of over one million cooking recipes and 13 million food images. As the largest publicly available collection of recipe data, Recipe1M+ affords the ability to train high-capacity modelson aligned, multimodal data. Using these data, we train a neural network to learn a joint embedding of recipes and images that yields impressive results on an image-recipe retrieval task. Moreover, we demonstrate that regularization via the addition of a high-level classification objective both improves retrieval performance to rival that of humans and enables semantic vector arithmetic. We postulate that these embeddings will provide a basis for further exploration of the Recipe1M+ dataset and food and cooking in general. Code, data and models are publicly available.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OmniFood-Bench: Evaluating VLMs for Nutrient Reasoning and Personalized Health Advice

    cs.AI 2026-07 conditional novelty 6.0 of 10

    VLMs show a Semantic-Physical Gap on food images: strong dish naming but high MAPE on mass/nutrients and frequent unsafe advice for high-risk disease profiles.

  2. ISO-Bench: Benchmarking Multimodal Causal Reasoning in Visual-Language Models through Procedural Plans

    cs.CL 2025-07 reject novelty 6.0 of 10

    ISO-Bench is presented as a benchmark for cross-modal causal reasoning, but its positive and negative examples are constructed from temporal position, allowing a non-causal image-text matching shortcut.

  3. HANDI: Hand-Centric Text-and-Image Conditioned Video Generation

    cs.CV 2024-12 conditional novelty 6.0 of 10

    HANDI generates hand-centric videos from an image and text prompt via automatic motion-area localization and a hand refinement loss.

  4. What am I missing here?: Evaluating Large Language Models for Masked Sentence Prediction

    cs.CL 2025-08 conditional novelty 5.0 of 10

    Commercial LLMs are poor at predicting a missing sentence in narrative and expository texts, though they perform better in structured procedural text.

  5. A Survey on Large Language Models in Multimodal Recommender Systems

    cs.IR 2025-05 conditional novelty 4.0 of 10

    A literature survey that categorizes LLM-based multimodal recommendation methods into prompting, training, and data-adaptation families and compiles datasets and metrics.

  6. CaLoRAify: Calorie Estimation with Visual-Text Pairing and LoRA-Driven Visual Language Models

    cs.CV 2024-12 reject novelty 3.0 of 10

    A LoRA-fine-tuned MiniGPT-v2 with USDA retrieval answers food and calorie questions from one image, but calorie accuracy is never measured.

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