The paper builds and human-labels a 3,782-video dataset spanning five hallucination categories in text-to-video outputs, and shows that standard classifiers reach only about 35% accuracy on the resulting classification task.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
ViBe: A Text-to-Video Benchmark for Evaluating Hallucination in Large Multimodal Models
The paper builds and human-labels a 3,782-video dataset spanning five hallucination categories in text-to-video outputs, and shows that standard classifiers reach only about 35% accuracy on the resulting classification task.