Multi-Level Multimodal Transformer Network for Multimodal Recipe Comprehension

Ao Liu, Shuai Yuan, Chenbin Zhang, Congjian Luo, Yaqing Liao, Kun Bai, Zenglin Xu
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引用次数: 6

Abstract

Multimodal Machine Comprehension ($\rm M^3C$) has been a challenging task that requires understanding both language and vision, as well as their integration and interaction. For example, the RecipeQA challenge, which provides several $\rm M^3C$ tasks, requires deep neural models to understand textual instructions, images of different steps, as well as the logic orders of food cooking. To address this challenge, we propose a Multi-Level Multi-Modal Transformer (MLMM-Trans) framework to integrate and understand multiple textual instructions and multiple images. Our model can conduct intensive attention mechanism at multiple levels of objects (e.g., step level and passage-image level) for sequences of different modalities. Experiments have shown that our model can achieve the state-of-the-art results on the three multimodal tasks of RecipeQA.
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多模态配方理解的多级多模态变压器网络
多模态机器理解一直是一项具有挑战性的任务,需要理解语言和视觉,以及它们的集成和交互。例如,RecipeQA挑战,它提供了几个任务,需要深度神经模型来理解文本指令,不同步骤的图像,以及食物烹饪的逻辑顺序。为了解决这一挑战,我们提出了一个多层次多模态转换器(MLMM-Trans)框架来整合和理解多个文本指令和多个图像。我们的模型可以针对不同模态的序列在物体的多个层次(例如步骤级和通道-图像级)上进行强化注意机制。实验表明,我们的模型可以在RecipeQA的三个多模态任务上达到最先进的结果。
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