Abstract
Humans perceive actions through key transitions that structure actions across multiple abstraction levels, whereas machines, relying on visual features, tend to over-segment. This highlights the difficulty of enabling hierarchical reasoning in video understanding. Interestingly, we observe that lower-level visual and high-level action latent variables evolve at different rates, with low-level visual variables changing rapidly, while high-level action variables evolve more slowly, making them easier to identify. Building on this insight, we propose the Hierarchical Action Learning (\textbf{HAL}) model for weakly-supervised action segmentation. Our approach introduces a hierarchical causal data generation process, where high-level latent action governs the dynamics of low-level visual features. To model these varying timescales effectively, we introduce deterministic processes to align these latent variables over time. The \textbf{HAL} model employs a hierarchical pyramid transformer to capture both visual features and latent variables, and a sparse transition constraint is applied to enforce the slower dynamics of high-level action variables. This mechanism enhances the identification of these latent variables over time. Under mild assumptions, we prove that these latent action variables are strictly identifiable. Experimental results on several benchmarks show that the \textbf{HAL} model significantly outperforms existing methods for weakly-supervised action segmentation, confirming its practical effectiveness in real-world applications.
Abstract (translated)
人类通过关键转换来感知动作，这些转换跨越多个抽象层次结构化了动作。相比之下，机器依赖视觉特征，往往会过度分割动作片段。这凸显了在视频理解中实现分层推理的难度。有趣的是，我们观察到低级视觉变量和高级动作潜在变量以不同的速度演变：低级视觉变量变化迅速，而高级行动变量则演变得更慢，使其更容易被识别。 基于这一洞察，我们提出了弱监督下的动作分割的层级动作学习（Hierarchical Action Learning, \textbf{HAL}）模型。我们的方法引入了一个分层因果数据生成过程，在此过程中，高层次潜在的动作支配低层次视觉特征的动力学变化。为了有效模拟这些不同的时间尺度，我们在不同时刻间引入了确定性过程来对齐潜在变量。 \textbf{HAL} 模型采用层级金字塔变压器来捕捉视觉特征和潜在变量，并施加稀疏过渡约束以强制执行高级动作变量的缓慢动态演变。此机制增强了长时间内识别这些潜在变量的能力。在适度假设下，我们证明了这些潜在的动作变量具有严格的可辨识性。 实验结果表明，在多个基准测试上，\textbf{HAL} 模型显著优于现有的弱监督动作分割方法，证实其在实际应用中的有效性和实用性。
URL
https://arxiv.org/abs/2602.24275
