Effective analysis of unusual domain specific video collections represents an important practical problem, where state-of-the-art general purpose models still face limitations. Hence, it is desirable to design benchmark datasets that challenge novel powerful models for specific domains with additional constraints. It is important to remember that domain specific data may be noisier (e.g., endoscopic or underwater videos) and often require more experienced users for effective search. In this paper, we focus on single-shot videos taken from moving cameras in underwater environments, which constitute a nontrivial challenge for research purposes. The first shard of a new Marine Video Kit dataset is presented to serve for video retrieval and other computer vision challenges. Our dataset is used in a special session during Video Browser Showdown 2023. In addition to basic meta-data statistics, we present several insights based on low-level features as well as semantic annotations of selected keyframes. The analysis also contains experiments showing limitations of respected general purpose models for retrieval. Our dataset and code are publicly available at \url{https://hkust-vgd.github.io/marinevideokit}.
翻译:有效分析非同寻常域特定视频收藏是一个重要的实际问题,在这方面,最先进的一般用途模型仍面临局限性。因此,最好设计基准数据集,对具有额外限制的特定领域具有挑战性的新强模型提出挑战。重要的是,要记住,具体域特定数据可能是隐性数据(例如内镜或水下视频),往往需要经验丰富的用户进行有效搜索。在本文中,我们侧重于从水下环境中移动相机拍摄的单张视频,这是研究目的面临的非三重挑战。新的海洋视频工具包数据集的第一块碎片,用于视频检索和其他计算机视觉挑战。我们的数据集在2023年视频浏览器Showdown期间的一次特别会议上使用。除了基本的元数据统计外,我们还根据低级别特征和选定关键框架的语义说明提出若干见解。分析还包含实验,显示受尊重的一般目的模型在检索方面的局限性。我们的数据集和代码可在以下网站公开查阅:https://hkust-vgd.githubio/marinetualki}。