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A unified spatial-temporal depth alignment framework that jointly resolves cross-view and cross-time inconsistencies without distinguishing foreground/background without distinguishing foreground/background is proposed.
This work presents a novel framework that addresses two critical limitations of existing methods: inadequate modeling of hierarchical temporal structure and inability to handle complex many-to-many correspondences between modalities by introducing a multi-scale temporal convolutional encoder that captures motion patter...
A cross-modal temporal alignment framework that combines a multi-scale temporal convolutional encoder with capsule-based dynamic routing, jointly optimizing temporal boundary prediction, cross-modal semantic alignment, and capsule diversity is proposed.
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