TITLE: MovieCuts: A New Dataset and Benchmark for Cut Type Recognition 

ABSTRACT: Understanding movies and their structural patterns is a crucial task in
decoding the craft of video editing. While previous works have developed tools
for general analysis, such as detecting characters or recognizing
cinematography properties at the shot level, less effort has been devoted to
understanding the most basic video edit, the Cut. This paper introduces the Cut
type recognition task, which requires modeling multi-modal information. To
ignite research in this new task, we construct a large-scale dataset called
MovieCuts, which contains 173,967 video clips labeled with ten cut types
defined by professionals in the movie industry. We benchmark a set of
audio-visual approaches, including some dealing with the problem's multi-modal
nature. Our best model achieves 47.7% mAP, which suggests that the task is
challenging and that attaining highly accurate Cut type recognition is an open
research problem. Advances in automatic Cut-type recognition can unleash new
experiences in the video editing industry, such as movie analysis for
education, video re-editing, virtual cinematography, machine-assisted trailer
generation, machine-assisted video editing, among others. Our data and code are
publicly available:
https://github.com/PardoAlejo/MovieCuts}{https://github.com/PardoAlejo/MovieCuts.
Understanding movies and their structural patterns is a crucial task in
decoding the craft of video editing. While previous works have developed tools
for general analysis, such as detecting characters or recognizing
cinematography properties at the shot level, less effort has been devoted to
understanding the most basic video edit, the Cut. This paper introduces the Cut
type recognition task, which requires modeling multi-modal information. To
ignite research in this new task, we construct a large-scale dataset called
MovieCuts, which contains 173,967 video clips labeled with ten cut types
defined by professionals in the movie industry. We benchmark a set of
audio-visual approaches, including some dealing with the problem's multi-modal
nature. Our best model achieves 47.7% mAP, which suggests that the task is
challenging and that attaining highly accurate Cut type recognition is an open
research problem. Advances in automatic Cut-type recognition can unleash new
experiences in the video editing industry, such as movie analysis for
education, video re-editing, virtual cinematography, machine-assisted trailer
generation, machine-assisted video editing, among others. Our data and code are
publicly available:
https://github.com/PardoAlejo/MovieCuts}{https://github.com/PardoAlejo/MovieCuts.
MovieCuts: A New Dataset and Benchmark for Cut Type Recognition
http://arxiv.org/abs/2109.05569v3