AVID is the first large-scale benchmark for audio-visual inconsistency detection, grounding, classification, and reasoning in long videos, constructed via agent-driven methods and showing that state-of-the-art models struggle while a fine-tuned baseline improves performance.
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Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
Adapts MDVLMs to TAL via planned training objective and step-level IoU reward, reporting gains over autoregressive baselines on ActivityNet and THUMOS datasets.
citing papers explorer
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AVID: A Benchmark for Omni-Modal Audio-Visual Inconsistency Understanding via Agent-Driven Construction
AVID is the first large-scale benchmark for audio-visual inconsistency detection, grounding, classification, and reasoning in long videos, constructed via agent-driven methods and showing that state-of-the-art models struggle while a fine-tuned baseline improves performance.
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Listening Deepfake Detection: A New Perspective Beyond Speaking-Centric Forgery Analysis
Introduces the LDD task, ListenForge dataset built from five listening head generation methods, and MANet model that detects listening forgeries via motion inconsistencies guided by audio semantics.
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Masked Diffusion Vision-Language Models for Temporal Action Localization
Adapts MDVLMs to TAL via planned training objective and step-level IoU reward, reporting gains over autoregressive baselines on ActivityNet and THUMOS datasets.