Generative models have demonstrated human-level proficiency in various benchmarks across domains like programming, natural sciences, and general knowledge.

Procedural activities are sequences of key-steps aimed at achieving specific goals. They are crucial to build intelligent agents able to assist users effectively.

The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising.

Specifically, we investigate two cases of such systems.

Temporal context plays a significant role in temporal action segmentation. In an offline setting, the context is typically captured by the segmentation network after observing the entire sequence. However, capturing and using such context information in an online setting remains an under-explored problem.

It suffers from performance degradation as the training goes on.