6 citations · 11 across the 9 of their papers we have counts for
10 papers
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…
Safe Multi-Agent Behavior Must Be Maintained, Not Merely Asserted: Constraint Drift in LLM-Based Multi-Agent Systems
Tianxiao Li, Yixing Ma, Haiquan Wen +4
Modern LLM based agents are no longer passive text generators. They read repositories, call tools, browse the web, execute code, maintain memory, communicate with other agents, and…
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…
Multimodal Hate Detection Using Dual-Stream Graph Neural Networks
Jiangbei Yue, Shuonan Yang, Tailin Chen +2
Hateful videos present serious risks to online safety and real-world well-being, necessitating effective detection methods. Although multimodal classification approaches integratin…
Revealing Temporal Label Noise in Multimodal Hateful Video Classification
Shuonan Yang, Tailin Chen, Rahul Singh +3
The rapid proliferation of online multimedia content has intensified the spread of hate speech, presenting critical societal and regulatory challenges. While recent work has advanc…
Enhanced Multimodal Hate Video Detection via Channel-wise and Modality-wise Fusion
Yinghui Zhang, Tailin Chen, Yuchen Zhang +1
The rapid rise of video content on platforms such as TikTok and YouTube has transformed information dissemination, but it has also facilitated the spread of harmful content, partic…