A Closer Look at Temporal Ordering in the Segmentation of Instructional Videos

TL;DR

This paper improves procedure segmentation and summarization in instructional videos by introducing a new order-aware metric, a constrained matching algorithm, and multi-modal training, leading to significant performance gains.

Contribution

It proposes an order-aware segmentation metric, a differentiable order-constrained matching algorithm, and multi-modal feature training for better instructional video analysis.

Findings

Improved segmentation accuracy by ~7% on YouCook2.

Enhanced summarization performance by ~2.5%.

Demonstrated effectiveness on two instructional video datasets.

Abstract

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS). In this paper, we take a closer look at PSS and propose three fundamental improvements over current methods. The segmentation task is critical, as generating a correct summary requires each step of the procedure to be correctly identified. However, current segmentation metrics often overestimate the segmentation quality because they do not consider the temporal order of segments. In our first contribution, we propose a new segmentation metric that takes into account the order of segments, giving a more reliable measure of the accuracy of a given predicted segmentation. Current PSS methods are typically trained by proposing segments, matching them with the ground truth and computing a loss. However, much like segmentation metrics, existing matching algorithms do not consider the temporal order of the mapping between candidate segments and the ground truth. In our second contribution, we propose a matching algorithm that constrains the temporal order of segment mapping, and is also differentiable. Lastly, we introduce multi-modal feature training for PSS, which further improves segmentation. We evaluate our approach on two instructional video datasets (YouCook2 and Tasty) and observe an improvement over the state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and summarization, respectively.