In this paper we present an approach for localizing steps of procedural
activities in narrated how-to videos. To deal with the scarcity of labeled data
at scale, we source the step descriptions from a language knowledge base
(wikiHow) containing instructional articles for a large variety of procedural
tasks. Without any form of manual supervision, our model learns to temporally
ground the steps of procedural articles in how-to videos by matching three
modalities: frames, narrations, and step descriptions. Specifically, our method
aligns steps to video by fusing information from two distinct pathways: i) {\em
direct} alignment of step descriptions to frames, ii) {\em indirect} alignment
obtained by composing steps-to-narrations with narrations-to-video
correspondences. Notably, our approach performs global temporal grounding of
all steps in an article at once by exploiting order information, and is trained
with step pseudo-labels which are iteratively refined and aggressively
filtered. In order to validate our model we introduce a new evaluation
benchmark -- HT-Step -- obtained by manually annotating a 124-hour subset of
HowTo100M\footnote{A test server is accessible at
https://eval.ai/web/challenges/challenge-page/2082.} with steps sourced
from wikiHow articles. Experiments on this benchmark as well as zero-shot
evaluations on CrossTask demonstrate that our multi-modality alignment yields
dramatic gains over several baselines and prior works. Finally, we show that
our inner module for matching narration-to-video outperforms by a large margin
the state of the art on the HTM-Align narration-video alignment benchmark.

本文提出了一种无需手动监督的方法来将『如何做』视频中的步骤与 wikiHow 等语言知识库中的指导文章进行对齐，并采用多模态对齐进行全局时间定位，验证结果表明与现有方法相比具有明显优势。