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

T. Fujii, Y. Sei, Yasuyuki Tahara, R. Orihara, Akihiko Ohsuga
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引用次数: 2

Abstract

Automatic captioning tasks that describe the content of images and moving images in natural language have important applications in areas such as search technology. In addition, captioning can assist with understanding content. Understanding of content can be deepened in a short time by reading captions. Among captioning models that use deep training, the encoder–decoder [1] model has generated considerable results and attracted attention, but many existing studies only consider the consistency of contiguous scenes over short periods. Considering the consistency of video segments as a matter of captioning has high importance. Generating cooking recipe sentences from cooking videos can be considered a captioning problem by treating recipes as captions. In addition, because the cooking video is constituted as a set of fragmentary tasks, a model that considers the consistency of the whole video is considered to be effective.
"不切胡萝卜,绝不炒胡萝卜",利用深度学习从烹饪视频中生成烹饪食谱,同时考虑到上一个过程
用自然语言描述图像和动态图像内容的自动字幕任务在搜索技术等领域有着重要的应用。此外,字幕还有助于理解内容。通过阅读字幕可以在短时间内加深对内容的理解。在使用深度训练的字幕模型中,编码器-解码器[1]模型取得了可观的成果并引起了关注,但现有的许多研究只考虑了短时间内连续场景的一致性。考虑视频片段的一致性对于字幕制作具有重要意义。从烹饪视频中生成烹饪菜谱句子可视为字幕问题,将菜谱视为字幕。此外,由于烹饪视频是由一系列片段任务构成的,因此考虑整个视频一致性的模型被认为是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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