Adaptive Latent Trajectory Anchoring for Action Segmentation Dataset Condensation

Dataset condensation for action segmentation synthesizescompact, informative representations of long, untrimmed video datasets.The existing approach relies on Variational Autoencoders and an iter-ative latent optimization; it is computationally expensive and suffersfrom over-smoothed reconstructions and rigid temporal constraints. Thispaper proposes to shift the condensation paradigm from optimization-based inversion to deterministic latent mapping. By leveraging Denois-ing Diffusion Implicit Models, we represent action segments as continu-ous trajectories anchored by sparse latent points in the noise manifold.To maximize representational efficiency, we introduce an adaptive allo-cation mechanism that dynamically redistributes the anchoring budgetbased on segment-wise reconstruction difficulty. Extensive experimentsdemonstrate that our framework significantly outperforms state-of-the-art methods in segmentation performance across common datasets. No-tably, our approach achieves performance parity with real data trainingwhile maintaining a condensation ratio of 2.4% on Breakfast dataset.