HateClipSeg：用于细粒度仇恨视频检测的片段级标注数据集
HateClipSeg: A Segment-Level Annotated Dataset for Fine-Grained Hate Video Detection
AI总结：
针对现有数据集缺乏细粒度标注导致视频仇恨言论检测困难的问题，本文构建了含片段级标注的多模态数据集HateClipSeg，提出三个基准任务，为细粒度仇恨视频检测提供资源与基准。
摘要：
由于多模态内容的复杂性以及现有数据集中缺乏细粒度标注，视频中的仇恨言论检测仍然具有挑战性。我们提出HateClipSeg，一个具有视频级和片段级标注的大规模多模态数据集，包含超过11714个片段，标注为正常或五个攻击性类别：仇恨、侮辱、性、暴力、自残，以及明确的目标受害者标签。我们的三阶段标注过程实现了高标注者间一致性（Krippendorff's alpha=0.817）。我们提出三个任务来基准测试性能：（1）修剪仇恨视频分类，（2）时间仇恨视频定位，（3）在线仇恨视频分类。结果显示当前模型存在显著差距，强调需要更复杂的多模态和时间感知方法。HateClipSeg数据集可在https://github.com/Social-AI-Studio/HateClipSeg.git公开获取。
英文摘要：
Detecting hate speech in videos remains challenging due to the complexity of multimodal content and the lack of fine-grained annotations in existing datasets. We present HateClipSeg, a large-scale multimodal dataset with both video-level and segment-level annotations, comprising over 11,714 segments labeled as Normal or across five Offensive categories: Hateful, Insulting, Sexual, Violence, Self-Harm, along with explicit target victim labels. Our three-stage annotation process yields high inter-annotator agreement (Krippendorff's alpha = 0.817). We propose three tasks to benchmark performance: (1) Trimmed Hateful Video Classification, (2) Temporal Hateful Video Localization, and (3) Online Hateful Video Classification. Results highlight substantial gaps in current models, emphasizing the need for more sophisticated multimodal and temporally aware approaches. The HateClipSeg dataset are publicly available at https://github.com/Social-AI-Studio/HateClipSeg.git.
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。
