# Marine Video Kit: A New Marine Video Dataset for Content-based Analysis and Retrieval

arXiv: https://arxiv.org/abs/2209.11518 · HTML: https://mlsanity.com/paper/2209.11518/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 24 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [Towards Automatic Learning of Procedures from Web Instructional Videos](https://mlsanity.com/paper/1703.09788/) (2017)
- [ClipCap: CLIP Prefix for Image Captioning](https://mlsanity.com/paper/2111.09734/) (2021)
- [A Joint Sequence Fusion Model for Video Question Answering and Retrieval](https://mlsanity.com/paper/1808.02559/) (2018)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [V3C - a Research Video Collection](https://mlsanity.com/paper/1810.04401/) (2018)

## Built on by (most cited first, 9 of 9)

- [A Survey of Video Datasets for Grounded Event Understanding](https://mlsanity.com/paper/2406.09646/) (2024)
- [Think Before You Segment: High-Quality Reasoning Segmentation with GPT Chain of Thoughts](https://mlsanity.com/paper/2503.07503/) (2025)
- [End-To-End Underwater Video Enhancement: Dataset and Model](https://mlsanity.com/paper/2403.11506/) (2024)
- [UnDIVE: Generalized Underwater Video Enhancement Using Generative Priors](https://mlsanity.com/paper/2411.05886/) (2024)
- [AUTV: Creating Underwater Video Datasets with Pixel-wise Annotations](https://mlsanity.com/paper/2503.12828/) (2025)
- [MarineVRS: Marine Video Retrieval System with Explainability via Semantic Understanding](https://mlsanity.com/paper/2306.04593/) (2023)
- [Experimental Evaluation of Static Image Sub-Region-Based Search Models Using CLIP](https://mlsanity.com/paper/2506.06938/) (2025)
- [MSC: A Marine Wildlife Video Dataset with Grounded Segmentation and Clip-Level Captioning](https://mlsanity.com/paper/2508.04549/) (2025)
- [Fusionista2.0: Efficiency Retrieval System for Large-Scale Datasets](https://mlsanity.com/paper/2511.12255/) (2025)

## Abstract (arXiv)

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.