  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 https://hkust-vgd.github.io/marinevideokit.

Computer Vision and Pattern Recognition;Information Retrieval;Multimedia