Short-form UGC video platforms, like Kwai and TikTok, have been an emerging
and irreplaceable mainstream media form, thriving on user-friendly engagement,
and kaleidoscope creation, etc. However, the advancing content-generation
modes, e.g., special effects, and sophisticated processing workflows, e.g.,
de-artifacts, have introduced significant challenges to recent UGC video
quality assessment: (i) the ambiguous contents hinder the identification of
quality-determined regions. (ii) the diverse and complicated hybrid distortions
are hard to distinguish. To tackle the above challenges and assist in the
development of short-form videos, we establish the first large-scale
Kaleidoscope short Video database for Quality assessment, termed KVQ, which
comprises 600 user-uploaded short videos and 3600 processed videos through the
diverse practical processing workflows, including pre-processing, transcoding,
and enhancement. Among them, the absolute quality score of each video and
partial ranking score among indistinguishable samples are provided by a team of
professional researchers specializing in image processing. Based on this
database, we propose the first short-form video quality evaluator, i.e., KSVQE,
which enables the quality evaluator to identify the quality-determined
semantics with the content understanding of large vision language models (i.e.,
CLIP) and distinguish the distortions with the distortion understanding module.
Experimental results have shown the effectiveness of KSVQE on our KVQ database
and popular VQA databases.

通过建立大规模的 Kaleidoscope 短视频数据库来解决 UGC 视频质量评估中的挑战，并提出了第一个短视频质量评估器 KSVQE，该评估器利用大规模视觉语言模型（如 CLIP）来理解视频内容并使用扭曲理解模块来区分扭曲，实验证明了 KSVQE 在 KVQ 数据库和热门 VQA 数据库中的有效性。

KVQ：短视频的万花筒视频质量评估

KVQ: Kaleidoscope Video Quality Assessment for Short-form Videos

While short-form videos head to reshape the entire social media landscape,
experts are exceedingly worried about their depressive impacts on viewers, as
evidenced by medical studies. To prevent widespread consequences, platforms are
eager to predict these videos' impact on viewers' mental health. Subsequently,
they can take intervention measures, such as revising recommendation algorithms
and displaying viewer discretion. Nevertheless, applicable predictive methods
lack relevance to well-established medical knowledge, which outlines clinically
proven external and environmental factors of depression. To account for such
medical knowledge, we resort to an emergent methodological discipline, seeded
Neural Topic Models (NTMs). However, existing seeded NTMs suffer from the
limitations of single-origin topics, unknown topic sources, unclear seed
supervision, and suboptimal convergence. To address those challenges, we
develop a novel Knowledge-guided Multimodal NTM to predict a short-form video's
depressive impact on viewers. Extensive empirical analyses using TikTok and
Douyin datasets prove that our method outperforms state-of-the-art benchmarks.
Our method also discovers medically relevant topics from videos that are linked
to depressive impact. We contribute to IS with a novel video analytics method
that is generalizable to other video classification problems. Practically, our
method can help platforms understand videos' mental impacts, thus adjusting
recommendations and video topic disclosure.

通过应用知识引导的多模态神经主题模型，我们成功地预测了短视频对观众的抑郁影响，并发现了与抑郁影响相关的医学主题。

短视频与心理健康：知识引导的多模态神经主题模型

Short-Form Videos and Mental Health: A Knowledge-Guided Multimodal  Neural Topic Model

The short-form videos have explosive popularity and have dominated the new
social media trends. Prevailing short-video platforms,~\textit{e.g.}, Kuaishou
(Kwai), TikTok, Instagram Reels, and YouTube Shorts, have changed the way we
consume and create content. For video content creation and understanding, the
shot boundary detection (SBD) is one of the most essential components in
various scenarios. In this work, we release a new public Short video sHot
bOundary deTection dataset, named SHOT, consisting of 853 complete short videos
and 11,606 shot annotations, with 2,716 high quality shot boundary annotations
in 200 test videos. Leveraging this new data wealth, we propose to optimize the
model design for video SBD, by conducting neural architecture search in a
search space encapsulating various advanced 3D ConvNets and Transformers. Our
proposed approach, named AutoShot, achieves higher F1 scores than previous
state-of-the-art approaches, e.g., outperforming TransNetV2 by 4.2%, when being
derived and evaluated on our newly constructed SHOT dataset. Moreover, to
validate the generalizability of the AutoShot architecture, we directly
evaluate it on another three public datasets: ClipShots, BBC and RAI, and the
F1 scores of AutoShot outperform previous state-of-the-art approaches by 1.1%,
0.9% and 1.2%, respectively. The SHOT dataset and code can be found in
this https URL .

本研究通过提取 853 个完整的短视频和 11,606 个镜头注释，利用新数据财富，提出了一种名为 AutoShot 的方法，通过在包含各种先进的 3D ConvNets 和 Transformer 的搜索空间中进行神经架构搜索来优化短视频镜头分割的模型设计，并在新构建的 SHOT 数据集上对该方法进行了推导和评估，实现了比先前最先进方法更高的 F1 分数，也在 ClipShots、BBC 和 RAI 数据集上获得了更好的表现。