Systems and methods for task progress estimation using a generative model with shuffled video inputs

A method implemented using one or more processors and comprising:

• generating, using a generative model, a sequence of task progress values for a corresponding sequence of video frames depicting one or more tasks, wherein the sequence of video frames is provided as input to the generative model in a shuffled temporal order, and wherein each task progress value in the sequence of task progress values is generated autoregressively based on previously generated task progress values in the sequence;
• determining a quality score for the corresponding sequence of video frames based on a correlation between the sequence of task progress values and an original temporal order of the sequence of video frames;
• and
• based on the quality score, selectively including the corresponding sequence of video frames in a training dataset for a separate model.

A method implemented using one or more processors and comprising:

• providing, as an input to a generative model, a shuffled sequence of video frames from a digital video and an indication of a task depicted in the digital video;
• generating, using the generative model, a sequence of task progress values, wherein each task progress value in the sequence of task progress values corresponds to a respective video frame in the shuffled sequence of video frames;
• determining a quality score for the digital video based on a correlation between the sequence of task progress values and an original temporal order of the sequence of video frames;
• and
• classifying the digital video as suitable or unsuitable for training a separate model based on the quality score.