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6 results on '"Tatsuki Fujii"'

3. 'Never fry carrots without chopping' Generating Cooking Recipes from Cooking Videos Using Deep Learning Considering Previous Process

Author

Tatsuki Fujii, Yuichi Sei, Yasuyuki Tahara, Ryohei Orihara, and Akihiko Ohsuga

Subjects
Image captioning, deep learning, cooking, recipe, Doc2Vec, Electronic computers. Computer science, QA75.5-76.95
Abstract

Research on deep-training captioning models that modify the natural-language contents of images and moving images has produced considerable results and attracted attention in recent years. In this research, we aim to generate recipe sentences from cooking videos acquired from YouTube. We treat this as an image-captioning task and propose two methods suitable for the work. We propose a method that adds a vector of a sentence already generated in the same recipe to the input of a captioning model. Then, we compare generated and correct sentences to calculate scores. We also propose a data-processing method to improve accuracy. We use several widely used metrics to evaluate image-captioning problems. We then train the same data with the simplest encoder–decoder model, compare it with correct recipe sentences, and calculate the metrics. The results indicate that our proposed methods help increase accuracy.

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6. 'Never fry carrots without chopping' Generating Cooking Recipes from Cooking Videos Using Deep Learning Considering Previous Process

Author

Yuichi Sei, Yasuyuki Tahara, Tatsuki Fujii, Akihiko Ohsuga, and Ryohei Orihara

Subjects
Engineering drawing, cooking, Computer Networks and Communications, Computer science, business.industry, Deep learning, Recipe, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Process (computing), deep learning, recipe, lcsh:QA75.5-76.95, Computer Science Applications, Artificial intelligence, Image captioning, lcsh:Electronic computers. Computer science, business, Doc2Vec
Abstract

Research on deep-training captioning models that modify the natural-language contents of images and moving images has produced considerable results and attracted attention in recent years. In this research, we aim to generate recipe sentences from cooking videos acquired from YouTube. We treat this as an image-captioning task and propose two methods suitable for the work. We propose a method that adds a vector of a sentence already generated in the same recipe to the input of a captioning model. Then, we compare generated and correct sentences to calculate scores. We also propose a data-processing method to improve accuracy. We use several widely used metrics to evaluate image-captioning problems. We then train the same data with the simplest encoder–decoder model, compare it with correct recipe sentences, and calculate the metrics. The results indicate that our proposed methods help increase accuracy.

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