Classifying videos into distinct categories, such as Sport and Music Video,
is crucial for multimedia understanding and retrieval, especially when an
immense volume of video content is being constantly generated. Traditional
methods require video decompression to extract pixel-level features like color,
texture, and motion, thereby increasing computational and storage demands.
Moreover, these methods often suffer from performance degradation in
low-quality videos. We present a novel approach that examines only the
post-compression bitstream of a video to perform classification, eliminating
the need for bitstream decoding. To validate our approach, we built a
comprehensive data set comprising over 29,000 YouTube video clips, totaling
6,000 hours and spanning 11 distinct categories. Our evaluations indicate
precision, accuracy, and recall rates consistently above 80%, many exceeding
90%, and some reaching 99%. The algorithm operates approximately 15,000 times
faster than real-time for 30fps videos, outperforming traditional Dynamic Time
Warping (DTW) algorithm by seven orders of magnitude.

通过检查视频的后压缩比特流进行分类，我们提出了一种新颖的方法，消除了对比特流解码的需求，并在性能上超越了传统的动态时间规整算法。

利用压缩帧大小实现超快速视频分类

Leveraging Compressed Frame Sizes For Ultra-Fast Video Classification

Text-to-video generation aims to produce a video based on a given prompt.
Recently, several commercial video models have been able to generate plausible
videos with minimal noise, excellent details, and high aesthetic scores.
However, these models rely on large-scale, well-filtered, high-quality videos
that are not accessible to the community. Many existing research works, which
train models using the low-quality WebVid-10M dataset, struggle to generate
high-quality videos because the models are optimized to fit WebVid-10M. In this
work, we explore the training scheme of video models extended from Stable
Diffusion and investigate the feasibility of leveraging low-quality videos and
synthesized high-quality images to obtain a high-quality video model. We first
analyze the connection between the spatial and temporal modules of video models
and the distribution shift to low-quality videos. We observe that full training
of all modules results in a stronger coupling between spatial and temporal
modules than only training temporal modules. Based on this stronger coupling,
we shift the distribution to higher quality without motion degradation by
finetuning spatial modules with high-quality images, resulting in a generic
high-quality video model. Evaluations are conducted to demonstrate the
superiority of the proposed method, particularly in picture quality, motion,
and concept composition.

基于给定提示生成视频的文本到视频生成旨在生成可信的视频。最近，一些商业视频模型能够生成具有最小噪声、出色细节和高美观分数的视频。然而，这些模型依赖于大规模、经过良好过滤、高质量的视频，而这对社区来说是不可获得的。许多现有的研究工作使用低质量的 WebVid-10M 数据集来训练模型，由于模型的优化是为了适应 WebVid-10M，因此很难生成高质量的视频。在这项工作中，我们探索了从稳定扩散中延伸的视频模型的训练方案，并研究了利用低质量视频和合成高质量图像获取高质量视频模型的可行性。我们首先分析视频模型的空间和时域模块与低质量视频的分布偏移之间的关联。我们观察到，与仅训练时域模块相比，所有模块的完整训练会导致空间和时域模块之间更强的耦合。基于这种更强的耦合，我们通过使用高质量图像微调空间模块来将分布向更高质量转移，而不会导致运动退化，从而获得通用的高质量视频模型。评估工作说明了所提方法的优越性，特别是在图片质量、动作和概念组合方面。