SphereVAD performs training-free video anomaly detection by recasting anomaly discrimination as von Mises-Fisher likelihood-ratio geodesic inference on the unit hypersphere using intermediate MLLM features, with Frechet mean centering, holistic scene attention, and spherical geodesic pulling.
Clustering on the unit hypersphere using von mises-fisher distributions.Journal of Machine Learning Research, 6(9)
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SphUnc decomposes uncertainty via hyperspherical von Mises-Fisher latents and performs causal identification through structural models on those latents.
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SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere
SphereVAD performs training-free video anomaly detection by recasting anomaly discrimination as von Mises-Fisher likelihood-ratio geodesic inference on the unit hypersphere using intermediate MLLM features, with Frechet mean centering, holistic scene attention, and spherical geodesic pulling.
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SphUnc: Hyperspherical Uncertainty Decomposition and Causal Identification via Information Geometry
SphUnc decomposes uncertainty via hyperspherical von Mises-Fisher latents and performs causal identification through structural models on those latents.