This research proposes a theoretical framework, CASTA, to analyze state transition dynamics within context-aware video embedding spaces, formalizing the embedding space as a Riemannian manifold with state transitions following geodesic flows modulated by contextual attention mechanisms.
Key findings
CASTA models video embeddings as traversing a dynamic manifold governed by context-conditioned transition operators.
Formalizes context-aware embedding manifolds with well-defined metric structure.
Analysis of state transition operators and their spectral properties.
Characterization of trajectory stability and convergence in embedding space.
Bounds on representation capacity for capturing procedural video dynamics.
Limitations & open questions
The framework's practical application is validated through experiments on procedural video understanding benchmarks, suggesting potential limitations in other video genres.