Abstract
Hateful videos present serious risks to online safety and real-world well-being, necessitating effective detection methods. Although multimodal classification approaches integrating information from several modalities outperform unimodal ones, they typically neglect that even minimal hateful content defines a video's category. Specifically, they generally treat all content uniformly, instead of emphasizing the hateful components. Additionally, existing multimodal methods cannot systematically capture structured information in videos, limiting the effectiveness of multimodal fusion. To address these limitations, we propose a novel multimodal dual-stream graph neural network model. It constructs an instance graph by separating the given video into several instances to extract instance-level features. Then, a complementary weight graph assigns importance weights to these features, highlighting hateful instances. Importance weights and instance features are combined to generate video labels. Our model employs a graph-based framework to systematically model structured relationships within and across modalities. Extensive experiments on public datasets show that our model is state-of-the-art in hateful video classification and has strong explainability. Code is available: this https URL.
Abstract (translated)
仇恨视频对在线安全和现实生活中的福祉构成严重威胁，因此需要有效的检测方法。虽然多模态分类方法通过结合来自多种模式的信息优于单模态方法，但它们通常忽视了即使是少量的仇恨内容也会定义一个视频的类别。具体来说，现有方法普遍将所有内容视为统一处理，而没有强调仇恨成分的重要性。此外，现有的多模态方法无法系统地捕捉视频中的结构化信息，限制了多模态融合的有效性。 为了解决这些局限性，我们提出了一种新颖的多模态双流图神经网络模型。该模型通过将给定的视频分割成多个实例来构建实例图，并提取出实例级别的特征。然后，一个互补权重图会赋予这些特征重要性权重，突出仇恨内容的实例。之后，根据重要性权重和实例特征组合生成视频标签。 我们的模型采用基于图的方法，系统地建模了多模态之间的结构化关系。在公共数据集上的广泛实验表明，我们的模型在仇恨视频分类方面处于行业领先水平，并具有很强的可解释性。代码可在以下链接获取：[提供URL]。
URL
https://arxiv.org/abs/2509.13515
