Leveraging Procedural Knowledge and Task Hierarchies for Efficient Instructional Video Pre-training
This work addresses the challenge of efficient instructional video recommendation for users seeking task-specific content, though it is incremental as it builds on existing methods with structured prior knowledge.
The paper tackles the problem of efficiently training models for instructional video understanding by leveraging mined task hierarchies and procedural steps as prior knowledge, resulting in improved performance on task recognition, step recognition, and step prediction tasks when pre-training data and compute are limited.
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.