Leveraging Procedural Knowledge and Task Hierarchies for Efficient Instructional Video Pre-training

TL;DR

This paper introduces Pivot, a pre-training approach for instructional videos that leverages task hierarchies and procedural knowledge to improve task and step recognition, especially when data and compute are limited.

Contribution

The paper presents a novel pre-training method that explicitly incorporates task hierarchies and procedural steps for instructional video understanding, enhancing efficiency and generalization.

Findings

Outperforms baselines in limited data scenarios

Effective in task and step recognition

Improves step prediction accuracy

Abstract

Instructional videos provide a convenient modality to learn new tasks (ex. cooking a recipe, or assembling furniture). A viewer will want to find a corresponding video that reflects both the overall task they are interested in as well as contains the relevant steps they need to carry out the task. To perform this, an instructional video model should be capable of inferring both the tasks and the steps that occur in an input video. Doing this efficiently and in a generalizable fashion is key when compute or relevant video topics used to train this model are limited. To address these requirements we explicitly mine task hierarchies and the procedural steps associated with instructional videos. We use this prior knowledge to pre-train our model, $\texttt{Pivot}$, for step and task prediction. During pre-training, we also provide video augmentation and early stopping strategies to optimally identify which model to use for downstream tasks. We test this pre-trained model on task recognition, step recognition, and step prediction tasks on two downstream datasets. When pre-training data and compute are limited, we outperform previous baselines along these tasks. Therefore, leveraging prior task and step structures enables efficient training of $\texttt{Pivot}$ for instructional video recommendation.