6 citations · 11 across the 9 of their papers we have counts for
8 papers · 1 filter
CLARA: Clip-Level Multimodal Alignment with VLM-Derived Rationales for Hateful Video Detection
Yuchen Zhang, Shuang Dai, Zeyu Fu +3
Hateful video detection has become increasingly important with the rapid growth of video-centric social media platforms, given the serious risks that hate speech poses to both indi…
Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
Tianxiao Li, Zhenglin Huang, Haiquan Wen +10
Multimodal deepfakes are proliferating on social media and threaten authenticity, information integrity, and digital forensics. Existing benchmarks are constrained by their single-…
Towards Training-free Multimodal Hate Localisation with Large Language Models
Yueming Sun, Long Yang, Jianbo Jiao +1
The proliferation of hateful content in online videos poses severe threats to individual well-being and societal harmony. However, existing solutions for video hate detection eithe…
MultiHateLoc: Towards Temporal Localisation of Multimodal Hate Content in Online Videos
Qiyue Sun, Tailin Chen, Yinghui Zhang +4
The rapid growth of video content on platforms such as TikTok and YouTube has intensified the spread of multimodal hate speech, where harmful cues emerge subtly and asynchronously…
Training-Free and Interpretable Hateful Video Detection via Multi-stage Adversarial Reasoning
Shuonan Yang, Yuchen Zhang, Zeyu Fu
Hateful videos pose serious risks by amplifying discrimination, inciting violence, and undermining online safety. Existing training-based hateful video detection methods are constr…
Reasoning-Aware Multimodal Fusion for Hateful Video Detection
Shuonan Yang, Tailin Chen, Jiangbei Yue +3
Hate speech in online videos is posing an increasingly serious threat to digital platforms, especially as video content becomes increasingly multimodal and context-dependent. Exist…