CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
Youku-mplug: A 10 million large-scale chinese video-language dataset for pre-training and benchmarks
3 Pith papers cite this work. Polarity classification is still indexing.
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AuDisAgent reformulates multimodal controversy detection as a dynamic audience dissemination process using screening, panel discussion, and arbitration agents, plus comment bootstrapping, and reports outperforming prior static methods on a public dataset.
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.
citing papers explorer
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CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection
CMTA detects AI-generated videos by capturing unnatural temporal stability in visual-textual semantic alignment via joint embeddings and multi-grained temporal modeling, outperforming prior methods in cross-generator tests.
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From Static Analysis to Audience Dissemination: A Training-Free Multimodal Controversy Detection Multi-Agent Framework
AuDisAgent reformulates multimodal controversy detection as a dynamic audience dissemination process using screening, panel discussion, and arbitration agents, plus comment bootstrapping, and reports outperforming prior static methods on a public dataset.
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ATSS: Detecting AI-Generated Videos via Anomalous Temporal Self-Similarity
ATSS detects AI-generated videos by measuring unnatural repetitive temporal correlations in triple similarity matrices derived from frame visuals and semantic descriptions.