RecipeGen: A Benchmark for Real-World Recipe Image Generation
- URL: http://arxiv.org/abs/2503.05228v1
- Date: Fri, 07 Mar 2025 08:25:28 GMT
- Title: RecipeGen: A Benchmark for Real-World Recipe Image Generation
- Authors: Ruoxuan Zhang, Hongxia Xie, Yi Yao, Jian-Yu Jiang-Lin, Bin Wen, Ling Lo, Hong-Han Shuai, Yung-Hui Li, Wen-Huang Cheng,
- Abstract summary: RecipeGen is the first real-world goal-step-image benchmark for recipe generation.<n>It features diverse ingredients, varied recipe steps, multiple cooking styles, and a broad collection of food categories.
- Score: 28.655663435450766
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recipe image generation is an important challenge in food computing, with applications from culinary education to interactive recipe platforms. However, there is currently no real-world dataset that comprehensively connects recipe goals, sequential steps, and corresponding images. To address this, we introduce RecipeGen, the first real-world goal-step-image benchmark for recipe generation, featuring diverse ingredients, varied recipe steps, multiple cooking styles, and a broad collection of food categories. Data is in https://github.com/zhangdaxia22/RecipeGen.
Related papers
- CookingDiffusion: Cooking Procedural Image Generation with Stable Diffusion [58.92430755180394]
We present textbfCookingDiffusion, a novel approach to generate photo-realistic images of cooking steps.<n>These prompts encompass text prompts, image prompts, and multi-modal prompts, ensuring the consistent generation of cooking procedural images.<n>Our experimental results demonstrate that our model excels at generating high-quality cooking procedural images.
arXiv Detail & Related papers (2025-01-15T06:58:53Z) - Retrieval Augmented Recipe Generation [96.43285670458803]
We propose a retrieval augmented large multimodal model for recipe generation.<n>It retrieves recipes semantically related to the image from an existing datastore as a supplement.<n>It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation.
arXiv Detail & Related papers (2024-11-13T15:58:50Z) - Counterfactual Recipe Generation: Exploring Compositional Generalization
in a Realistic Scenario [60.20197771545983]
We design the counterfactual recipe generation task, which asks models to modify a base recipe according to the change of an ingredient.
We collect a large-scale recipe dataset in Chinese for models to learn culinary knowledge.
Results show that existing models have difficulties in modifying the ingredients while preserving the original text style, and often miss actions that need to be adjusted.
arXiv Detail & Related papers (2022-10-20T17:21:46Z) - A Large-Scale Benchmark for Food Image Segmentation [62.28029856051079]
We build a new food image dataset FoodSeg103 (and its extension FoodSeg154) containing 9,490 images.
We annotate these images with 154 ingredient classes and each image has an average of 6 ingredient labels and pixel-wise masks.
We propose a multi-modality pre-training approach called ReLeM that explicitly equips a segmentation model with rich and semantic food knowledge.
arXiv Detail & Related papers (2021-05-12T03:00:07Z) - Revamping Cross-Modal Recipe Retrieval with Hierarchical Transformers
and Self-supervised Learning [17.42688184238741]
Cross-modal recipe retrieval has recently gained substantial attention due to the importance of food in people's lives.
We propose a simplified end-to-end model based on well established and high performing encoders for text and images.
Our proposed method achieves state-of-the-art performance in the cross-modal recipe retrieval task on the Recipe1M dataset.
arXiv Detail & Related papers (2021-03-24T10:17:09Z) - Structure-Aware Generation Network for Recipe Generation from Images [142.047662926209]
We investigate an open research task of generating cooking instructions based on only food images and ingredients.
Target recipes are long-length paragraphs and do not have annotations on structure information.
We propose a novel framework of Structure-aware Generation Network (SGN) to tackle the food recipe generation task.
arXiv Detail & Related papers (2020-09-02T10:54:25Z) - Multi-modal Cooking Workflow Construction for Food Recipes [147.4435186953995]
We build MM-ReS, the first large-scale dataset for cooking workflow construction.
We propose a neural encoder-decoder model that utilizes both visual and textual information to construct the cooking workflow.
arXiv Detail & Related papers (2020-08-20T18:31:25Z) - RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and
Evaluation System [29.150333060513177]
We present RecipeGPT, a novel online recipe generation and evaluation system.
System provides two modes of text generations: instruction generation from given recipe title and ingredients; and ingredient generation from recipe title and cooking instructions.
Back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset.
arXiv Detail & Related papers (2020-03-05T09:25:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.