用于动作分割数据集压缩的自适应潜在轨迹锚定
Adaptive Latent Trajectory Anchoring for Action Segmentation Dataset Condensation
浏览论文内容
中文总结 AI 辅助
该研究针对动作分割数据集压缩问题，提出利用去噪扩散隐式模型，将动作段表示为噪声流形中由稀疏潜在点锚定连续轨迹的方法，并引入自适应分配机制。实验表明，该框架显著优于现有方法，在保持低压缩率时实现与真实数据训练相当的性能。
中文摘要 AI 辅助
动作分割的数据集压缩用于合成长的未修剪视频数据集的紧凑、信息丰富的表示。现有方法依赖变分自编码器和迭代潜在优化，计算成本高，存在重建过度平滑和时间约束刚性的问题。本文提出将压缩范式从基于优化的反演转变为确定性潜在映射。利用去噪扩散隐式模型，将动作段表示为噪声流形中由稀疏潜在点锚定的连续轨迹。引入自适应分配机制，根据段级重建难度动态重新分配锚定预算。大量实验表明，该框架在常见数据集的分割性能上显著优于现有方法。特别是，该方法在保持早餐数据集2.4%压缩率的同时，实现了与真实数据训练相当的性能。
英文摘要
Dataset condensation for action segmentation synthesizes compact, informative representations of long, untrimmed video datasets. The existing approach relies on Variational Autoencoders and an iterative latent optimization; it is computationally expensive and suffers from over-smoothed reconstructions and rigid temporal constraints. This paper proposes to shift the condensation paradigm from optimization-based inversion to deterministic latent mapping. By leveraging Denoising Diffusion Implicit Models, we represent action segments as continuous trajectories anchored by sparse latent points in the noise manifold. To maximize representational efficiency, we introduce an adaptive allocation mechanism that dynamically redistributes the anchoring budget based on segment-wise reconstruction difficulty. Extensive experiments demonstrate that our framework significantly outperforms state-of-the-art methods in segmentation performance across common datasets. Notably, our approach achieves performance parity with real data training while maintaining a condensation ratio of 2.4\% on Breakfast dataset.
发表机构
- National University of Singapore（新加坡国立大学）
- ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
