Understanding and comprehending video content is crucial for many real-world applications such as search and recommendation systems. While recent progress of deep learning has boosted performance on various tasks using visual cues, deep cognition to reason intentions, motivation, or causality remains challenging. Existing datasets that aim to examine video reasoning capability focus on visual signals such as actions, objects, relations, or could be answered utilizing text bias. Observing this, we propose a novel task, along with a new dataset: Trope Understanding in Movies and Animations (TrUMAn), intending to evaluate and develop learning systems beyond visual signals. Tropes are frequently used storytelling devices for creative works. By coping with the trope understanding task and enabling the deep cognition skills of machines, we are optimistic that data mining applications and algorithms could be taken to the next level. To tackle the challenging TrUMAn dataset, we present a Trope Understanding and Storytelling (TrUSt) with a new Conceptual Storyteller module, which guides the video encoder by performing video storytelling on a latent space. The generated story embedding is then fed into the trope understanding model to provide further signals. Experimental results demonstrate that state-of-the-art learning systems on existing tasks reach only 12.01% of accuracy with raw input signals. Also, even in the oracle case with human-annotated descriptions, BERT contextual embedding achieves at most 28% of accuracy. Our proposed TrUSt boosts the model performance and reaches 13.94% performance. We also provide detailed analysis to pave the way for future research. TrUMAn is publicly available at:https://www.cmlab.csie.ntu.edu.tw/project/trope
翻译:理解和理解视频内容对于许多真实世界应用(如搜索和建议系统)至关重要。虽然最近深层次学习的进展提高了使用视觉提示、深刻理解理性意图、动机或因果关系等不同任务的业绩,但挑战性仍然存在。现有的数据集旨在审查视频推理能力,重点是动作、对象、关系等视觉信号,或利用文字偏差可以解答。我们观察了这个新任务,同时提出了一个新的数据集:电影和动画中的Trope理解(TrUMAn),目的是评估和开发视觉信号以外的学习系统。Trope经常使用故事描述设备进行创造性工作。通过应对轨迹理解任务,并扶持机器深层次的认知技能,我们乐观地认为数据挖掘应用和算法可以达到下一个水平。为了应对具有挑战性的TrUMAN数据集,我们提出一个Trope理解和童话(TrUSt), 新的概念缩略图模模块(TrUST),该模块将指导视频解析码系统,通过在隐性空间上进行进一步视频故事描述。Trope理解Tropeal 01 和最深层次分析,然后将原始任务嵌入到Trumexalexal exexexal exal ex exal exde 。随后,我们学习了当前系统。