解码多模态线索：揭示仇恨视频背后的隐含意义
Decoding Multimodal Cues: Unveiling the Implicit Meaning Behind Hateful Videos
AI总结：
提出IARE框架，通过信息增强和推理优化实现可解释的仇恨视频检测，在Ex-HateMM和Ex-ImpliHateVid数据集上达到最优性能。
摘要：
仇恨视频在在线平台上日益普遍，凸显了有效检测的迫切需求。然而，现有研究主要关注二元分类，未能提供揭示这些判断背后隐含意义的上下文理由，严重削弱了模型的可解释性。为填补这一空白，我们旨在实现可解释的仇恨视频检测，使模型能够提供整合相关证据和逻辑推理的上下文理由，同时做出决策。这种方法可以全面增强对视频内容的理解以及决策过程的可解释性。我们首先引入了两个用于可解释仇恨视频检测的数据集Ex-HateMM和Ex-ImpliHateVid。每个数据集提供了多模态有害元素的细粒度标注以及上下文理由。然后，我们提出了一个用于可解释检测的信息增强与推理优化（IARE）框架。该框架采用信息增强阶段，利用多模态思维链整合有害元素，从而丰富理由证据。此外，IARE包含一个推理优化阶段，其中直接偏好优化引导模型走向正确的推理路径并远离错误的路径，从而提高其理由的逻辑连贯性。我们在两个数据集上进行了大量实验，将多个基线与我们提出的IARE框架进行比较。结果表明，IARE在生成准确理由的同时实现了最先进的性能。
英文摘要：
Hateful videos have become prevalent on online platforms, highlighting an urgent need for effective detection. However, existing studies primarily focus on binary classification and fail to provide contextual rationales that reveal the implicit meanings behind these judgments, significantly undermining model explainability. To fill this gap, we aim to achieve explainable hateful video detection, enabling models to provide contextual rationales that integrate relevant evidence and logical reasoning alongside decisions. This approach can comprehensively enhance the understanding of video content and the explainability of the decision-making process. We first introduce two datasets, Ex-HateMM and Ex-ImpliHateVid, for explainable hateful video detection. Each dataset provides fine-grained annotations of multimodal harmful elements, along with contextual rationales. We then propose an Information Augmentation and Reasoning Enhancement (IARE) framework designed for explainable detection. The framework employs an information augmentation phase that leverages the multimodal chain-of-thought to integrate harmful elements, thereby enriching rationale evidence. Additionally, IARE incorporates a reasoning enhancement phase, in which Direct Preference Optimization guides the model toward correct reasoning paths and away from incorrect ones, thereby improving the logical coherence of its justifications. We conduct extensive experiments on the two datasets, comparing multiple baselines with our proposed IARE framework. The results demonstrate that IARE achieves state-of-the-art performance while also generating accurate rationales.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
