Investments in movie production are associated with a high level of risk as
movie revenues have long-tailed and bimodal distributions. Accurate prediction
of box-office revenue may mitigate the uncertainty and encourage investment.
However, learning effective representations for actors, directors, and
user-generated content-related keywords remains a challenging open problem. In
this work, we investigate the effects of self-supervised pretraining and
propose visual grounding of content keywords in objects from movie posters as a
pertaining objective. Experiments on a large dataset of 35,794 movies
demonstrate significant benefits of self-supervised training and visual
grounding. In particular, visual grounding pretraining substantially improves
learning on movies with content keywords and achieves 14.5% relative
performance gains compared to a finetuned BERT model with identical
architecture.

本文探讨了自我监督预训练和视觉定位内容关键字的效果，结果表明与相同架构的微调 BERT 模型相比，视觉定位预训练可以显著提高学习能力，并实现 14.5％的相对性能提升。

自监督和视觉基础预训练的电影票房预测

Movie Box Office Prediction With Self-Supervised and Visually Grounded  Pretraining

Animating a static face image with target facial expressions and movements is
important in the area of image editing and movie production. This face
reenactment process is challenging due to the complex geometry and movement of
human faces. Previous work usually requires a large set of images from the same
person to model the appearance. In this paper, we present a one-shot face
reenactment model, FaR-GAN, that takes only one face image of any given source
identity and a target expression as input, and then produces a face image of
the same source identity but with the target expression. The proposed method
makes no assumptions about the source identity, facial expression, head pose,
or even image background. We evaluate our method on the VoxCeleb1 dataset and
show that our method is able to generate a higher quality face image than the
compared methods.

本文提出了一种只需要输入一个源标识符和目标表情，便可生成一定质量的表情图片的一次性脸部再现模型，FaR-GAN，适用于图片处理和电影制作。将此方法应用于 VoxCeleb1 数据集并与其他方法进行比较取得更好的效果。

FaR-GAN 用于一次性人脸再现

FaR-GAN for One-Shot Face Reenactment

Audio Description (AD) provides linguistic descriptions of movies and allows
visually impaired people to follow a movie along with their peers. Such
descriptions are by design mainly visual and thus naturally form an interesting
data source for computer vision and computational linguistics. In this work we
propose a novel dataset which contains transcribed ADs, which are temporally
aligned to full length movies. In addition we also collected and aligned movie
scripts used in prior work and compare the two sources of descriptions. In
total the Large Scale Movie Description Challenge (LSMDC) contains a parallel
corpus of 118,114 sentences and video clips from 202 movies. First we
characterize the dataset by benchmarking different approaches for generating
video descriptions. Comparing ADs to scripts, we find that ADs are indeed more
visual and describe precisely what is shown rather than what should happen
according to the scripts created prior to movie production. Furthermore, we
present and compare the results of several teams who participated in a
challenge organized in the context of the workshop "Describing and
Understanding Video & The Large Scale Movie Description Challenge (LSMDC)", at
ICCV 2015.

本文提出了一个新的数据集，用于从音频注释中生成视频描述。该数据集包含 118,114 个句子和来自 202 部电影的视频剪辑。对比于脚本描述，我们发现 Audio Description 更加直观且准确的描述了电影中所呈现的内容。此外，我们呈现和比较了几个参加 “理解视频和大规模电影描述挑战” 的团队的结果，该挑战在 ICCV 2015 中举行。