CAST: Modeling Visual State Transitions for Consistent Video Retrieval
Abstract
As video content creation shifts towards long-form narratives, retrieving and composing short clips into coherent storylines becomes a critical challenge.
Standard retrieval formulations, however, perform context-agnostic retrieval, prioritizing local semantic alignment while neglecting procedural state and identity consistency across the narrative flow.
To address this, we introduce the task of Consistent Video Retrieval (CVR) and establish a benchmark designed to diagnose such inconsistencies via semantic hard negatives.
We propose CAST (Context-Aware State Transition), a lightweight adapter that models procedural progression as state-conditioned transitions.
Conditioned on visual history, CAST predicts a gated residual vector ($\Delta$) to selectively update the state embedding, ensuring procedural coherence while preserving identity.
Extensive experiments demonstrate that CAST significantly outperforms standard retrieval baselines on our CVR benchmark.
Furthermore, we show its potential as a plug-and-play consistency verifier, guiding black-box generation models (e.g., Veo) toward coherent video continuations within long-form narratives.
Standard retrieval formulations, however, perform context-agnostic retrieval, prioritizing local semantic alignment while neglecting procedural state and identity consistency across the narrative flow.
To address this, we introduce the task of Consistent Video Retrieval (CVR) and establish a benchmark designed to diagnose such inconsistencies via semantic hard negatives.
We propose CAST (Context-Aware State Transition), a lightweight adapter that models procedural progression as state-conditioned transitions.
Conditioned on visual history, CAST predicts a gated residual vector ($\Delta$) to selectively update the state embedding, ensuring procedural coherence while preserving identity.
Extensive experiments demonstrate that CAST significantly outperforms standard retrieval baselines on our CVR benchmark.
Furthermore, we show its potential as a plug-and-play consistency verifier, guiding black-box generation models (e.g., Veo) toward coherent video continuations within long-form narratives.
Research Areas
