  Over the last decade, online lecture videos have become increasingly popular
and have experienced a meteoric rise during the pandemic. However,
video-language research has primarily focused on instructional videos or
movies, and tools to help students navigate the growing online lectures are
lacking. Our first contribution is to facilitate research in the educational
domain, by introducing AVLectures, a large-scale dataset consisting of 86
courses with over 2,350 lectures covering various STEM subjects. Each course
contains video lectures, transcripts, OCR outputs for lecture frames, and
optionally lecture notes, slides, assignments, and related educational content
that can inspire a variety of tasks. Our second contribution is introducing
video lecture segmentation that splits lectures into bite-sized topics that
show promise in improving learner engagement. We formulate lecture segmentation
as an unsupervised task that leverages visual, textual, and OCR cues from the
lecture, while clip representations are fine-tuned on a pretext self-supervised
task of matching the narration with the temporally aligned visual content. We
use these representations to generate segments using a temporally consistent
1-nearest neighbor algorithm, TW-FINCH. We evaluate our method on 15 courses
and compare it against various visual and textual baselines, outperforming all
of them. Our comprehensive ablation studies also identify the key factors
driving the success of our approach.
